{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer R-Matrix\n",
    "\n",
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/candidate/R-Matrix.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\"\n",
    "\n",
    "# Set it to True if you don't want the optimizer to use the review logs from suspended cards.\n",
    "filter_out_suspended_cards = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import math\n",
    "from typing import List, Optional\n",
    "from datetime import timedelta, datetime\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import Tensor\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from itertools import accumulate\n",
    "from tqdm.auto import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deck file extracted successfully!\n",
      "revlog.csv saved.\n",
      "Trainset saved.\n",
      "Retention calculated.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b05bc7d13664949bd1839c0621e82ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/26286 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07727fcfc5b64e4ea4a0f8795b065738",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "analysis:   0%|          | 0/489 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis saved!\n",
      "1:again, 2:hard, 3:good, 4:easy\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.1          0.892        1.0     inf       2063\n",
      "            1,3           3.1          0.920        4.0    4.00       1600\n",
      "          1,3,3           7.1          0.910        7.9    1.98       1320\n",
      "        1,3,3,3          16.6          0.862       10.8    1.37        986\n",
      "      1,3,3,3,3          36.5          0.840       22.3    2.06        659\n",
      "    1,3,3,3,3,3          77.0          0.861       36.4    1.63        358\n",
      "  1,3,3,3,3,3,3         117.9          0.906       38.5    1.06        177\n",
      "              2           1.0          0.902        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.2    7.45        201\n",
      "          2,3,3          11.4          0.890        7.6    0.93        162\n",
      "              3           1.1          0.977        5.0     inf       4669\n",
      "            3,3           3.3          0.967       12.0    2.40       4211\n",
      "          3,3,3           8.1          0.960       19.9    1.66       3757\n",
      "        3,3,3,3          18.3          0.941       41.5    2.09       2834\n",
      "      3,3,3,3,3          35.2          0.930       50.5    1.22       1761\n",
      "    3,3,3,3,3,3          64.9          0.929       97.9    1.94       1102\n",
      "  3,3,3,3,3,3,3         100.9          0.949      143.0    1.46        530\n",
      "3,3,3,3,3,3,3,3         133.6          0.990     1821.9   12.74        131\n",
      "              4           2.0          0.976       10.7     inf       2789\n",
      "            4,3           3.7          0.976       20.4    1.91       2293\n",
      "          4,3,3           8.3          0.967       32.5    1.59       2051\n",
      "        4,3,3,3          19.8          0.958       54.5    1.68       1700\n",
      "      4,3,3,3,3          41.0          0.958       99.5    1.83       1148\n",
      "    4,3,3,3,3,3          68.1          0.956      116.1    1.17        521\n",
      "  4,3,3,3,3,3,3          78.5          0.979      110.7    0.95        248\n",
      "4,3,3,3,3,3,3,3         114.1          0.984      177.8    1.61        166\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Step 1\"\"\"\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Deck file extracted successfully!\")\n",
    "\n",
    "\"\"\"Step 2\"\"\"\n",
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(f\"\"\"\n",
    "SELECT *\n",
    "FROM revlog\n",
    "WHERE cid IN (\n",
    "    SELECT id\n",
    "    FROM cards\n",
    "    {\"WHERE queue != -1\" if filter_out_suspended_cards else \"\"}\n",
    ")\n",
    "\"\"\"\n",
    ")\n",
    "revlog = res.fetchall()\n",
    "if len(revlog) == 0:\n",
    "    raise Exception(\"No review log found!\")\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl', 'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000)].copy()\n",
    "\n",
    "df_set_due_date = df[(df['type'] == 4) & (df['ivl'] > 0)]\n",
    "df.drop(df_set_due_date.index, inplace=True)\n",
    "\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize('UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize('UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "\n",
    "df['is_learn_start'] = (df['type'] == 0) & (df['type'].shift() != 0)\n",
    "df['sequence_group'] = df['is_learn_start'].cumsum()\n",
    "last_learn_start = df[df['is_learn_start']].groupby('cid')['sequence_group'].last()\n",
    "df['last_learn_start'] = df['cid'].map(last_learn_start).fillna(0).astype(int)\n",
    "df['mask'] = df['last_learn_start'] <= df['sequence_group']\n",
    "df = df[df['mask'] == True].copy()\n",
    "df.drop(columns=['is_learn_start', 'sequence_group', 'last_learn_start', 'mask'], inplace=True)\n",
    "df = df[(df['type'] != 4)].copy()\n",
    "\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "\n",
    "df = df[(df['type'] != 3) | (df['factor'] != 0)].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=int(next_day_starts_at))\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['i'] = df.groupby('cid').cumcount() + 1\n",
    "df.loc[df['i'] == 1, 'delta_t'] = 0\n",
    "df = df.groupby('cid').filter(lambda group: group['type'].iloc[0] == 0)\n",
    "df['prev_type'] = df.groupby('cid')['type'].shift(1).fillna(0).astype(int)\n",
    "df['helper'] = ((df['type'] == 0) & ((df['prev_type'] == 1) | (df['prev_type'] == 2)) & (df['i'] > 1)).astype(int)\n",
    "df['helper'] = df.groupby('cid')['helper'].cumsum()\n",
    "df = df[df['helper'] == 0]\n",
    "del df['prev_type']\n",
    "del df['helper']\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "t_history = df.groupby('cid', group_keys=False)['delta_t'].apply(lambda x: cum_concat([[int(i)] for i in x]))\n",
    "df['t_history']=[','.join(map(str, item[:-1])) for sublist in t_history for item in sublist]\n",
    "r_history = df.groupby('cid', group_keys=False)['r'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['r_history']=[','.join(map(str, item[:-1])) for sublist in r_history for item in sublist]\n",
    "df = df.groupby('cid').filter(lambda group: group['id'].min() > time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000)\n",
    "df['y'] = df['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x])\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "df['retention'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['y'].transform('mean')\n",
    "df['total_cnt'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['id'].transform('count')\n",
    "print(\"Retention calculated.\")\n",
    "\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history', 'y'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in tqdm(df.index, desc=\"analysis\"):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)].copy()\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"Analysis saved!\")\n",
    "    caption = \"1:again, 2:hard, 3:good, 4:easy\\n\"\n",
    "    analysis = df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False)\n",
    "    print(caption + analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2, -0.2, 0.2, 1, 0.9, 0.5]\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w: List[float]):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w, dtype=torch.float32))\n",
    "        self.r_matrix = None\n",
    "\n",
    "    def stability_after_success(self, s: Tensor, new_d: Tensor, r: Tensor) -> Tensor:\n",
    "        new_s = s * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(s, self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "\n",
    "    def stability_after_failure(self, s: Tensor, new_d: Tensor, r: Tensor) -> Tensor:\n",
    "        new_s = self.w[9] * torch.pow(new_d, self.w[10]) * torch.pow(\n",
    "            s, self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X: Tensor, state: Tensor) -> Tensor:\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] + self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r_theoretical = torch.exp(np.log(0.9) * X[:,0] / state[:,0]).clamp(0.0001, 0.9999)\n",
    "            r_m, count = self.query_r_matrix(X[:,0].clamp(0.1, 36500), X[:,1], r_theoretical)\n",
    "            weight = self.w[13] * torch.pow(torch.tensor(count / 300), self.w[14])\n",
    "            r_w = (weight * r_m + (1 - weight) * r_theoretical).clamp(0.0001, 0.9999)\n",
    "            s_w = (X[:,0] * torch.log(r_theoretical) / torch.log(r_w)).clamp(0.1, 36500)\n",
    "            new_d = state[:,1] + self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(s_w, new_d, r_w), self.stability_after_failure(s_w, new_d, r_w))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs: Tensor, state: Optional[Tensor]=None) -> Tensor:\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init: Tensor, current: Tensor) -> Tensor:\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "    def calc_r_matrix(self, train_set):\n",
    "        outputs, _ = self.forward(train_set.x_train.transpose(0, 1))\n",
    "        stabilities, difficulties = outputs[train_set.seq_len-1, torch.arange(len(train_set))].transpose(0, 1)\n",
    "        p = torch.exp(np.log(0.9) * train_set.t_train / stabilities)\n",
    "        y = train_set.y_train\n",
    "        r_matrix_raw = pd.DataFrame({'s': stabilities.detach().numpy(), 'd': difficulties.detach().numpy(), 'p': p.detach().numpy(), 'y': y.detach().numpy()})\n",
    "        r_matrix_raw['s_bin'] = r_matrix_raw['s'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "        r_matrix_raw['d_bin'] = r_matrix_raw['d'].map(lambda x: int(round(x)))\n",
    "        r_matrix_raw['r_bin'] = r_matrix_raw['p'].map(lambda x: round(x * 4, 1) / 4)\n",
    "        R_Matrix = r_matrix_raw.groupby(by=['s_bin', 'd_bin', 'r_bin']).agg({'y': ['mean', 'count']}).reset_index()\n",
    "        R_Matrix.columns = ['S_rounded', 'D_rounded', 'R_t_rounded', 'R_measured', 'count']\n",
    "        R_Matrix.set_index(['S_rounded', 'D_rounded', 'R_t_rounded'], inplace=True)\n",
    "        self.r_matrix = R_Matrix\n",
    "\n",
    "    def query_r_matrix(self, s: Tensor, d: Tensor, r: Tensor) -> float:\n",
    "        if self.r_matrix is None:\n",
    "            return r, torch.zeros_like(r, dtype=torch.int64)\n",
    "        s_bins = list(map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2), s.detach().numpy()))\n",
    "        d_bins = list(map(lambda x: int(round(x)), d.detach().numpy()))\n",
    "        r_bins = list(map(lambda x: round(x * 4, 1) / 4, r.detach().numpy()))\n",
    "        index_tuples = list(zip(s_bins, d_bins, r_bins))\n",
    "        index_tuples_multi = pd.MultiIndex.from_tuples(index_tuples, names=[\"S_rounded\", \"D_rounded\", \"R_t_rounded\"])\n",
    "        exist_index = index_tuples_multi.isin(self.r_matrix.index)\n",
    "        r_m = r.clone()\n",
    "        r_m[exist_index] = torch.tensor(self.r_matrix.loc[index_tuples_multi[exist_index], 'R_measured'].values)\n",
    "        count = torch.ones_like(r, dtype=torch.int64)\n",
    "        count[exist_index] = torch.tensor(self.r_matrix.loc[index_tuples_multi[exist_index], 'count'].values.clip(max=300))\n",
    "        return r_m, count\n",
    "        \n",
    "\n",
    "class WeightClipper:\n",
    "    def __init__(self, frequency: int=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(-5, -0.1)\n",
    "            w[4] = w[4].clamp(-5, -0.1)\n",
    "            w[5] = w[5].clamp(0.05, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(-0.8, -0.15)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(-2, -0.01)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            w[13] = w[13].clamp(0.01, 0.99)\n",
    "            w[14] = w[14].clamp(0.01, 0.99)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line: str) -> Tensor:\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe: pd.DataFrame):\n",
    "        if dataframe.empty:\n",
    "            raise ValueError('Training data is inadequate.')\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "\n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source: RevlogDataset, batch_size: int):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            if remainder == 0:\n",
    "                self.batch_indices = np.split(indices, full_batches)\n",
    "            else:\n",
    "                self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder > 0:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from (self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist())\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Trainer:\n",
    "    def __init__(self, train_set: pd.DataFrame, test_set: pd.DataFrame, init_w: List[float], n_epoch: int=1, lr: float=1e-2, batch_size: int=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set: pd.DataFrame, test_set: pd.DataFrame):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self, verbose: bool=True):\n",
    "        # pretrain\n",
    "        best_loss = np.inf\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        pbar = tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader) * self.n_epoch)\n",
    "        for k in range(self.n_epoch):\n",
    "            self.model.calc_r_matrix(self.train_set)\n",
    "            for i, batch in enumerate(self.pre_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                difficulties = outputs[seq_lens-1, torch.arange(real_batch_size), 1]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                r_m, count = self.model.query_r_matrix(stabilities, difficulties, retentions)\n",
    "                weight = self.model.w[13] * torch.pow(torch.tensor(count / 300), self.model.w[14])\n",
    "                r_w = (weight * r_m + (1 - weight) * retentions).clamp(0.0001, 0.9999)\n",
    "                loss = self.loss_fn(r_w, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(n=real_batch_size)\n",
    "\n",
    "        pbar.close()\n",
    "        for name, param in self.model.named_parameters():\n",
    "            tqdm.write(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "        for k in range(self.n_epoch):\n",
    "            self.model.calc_r_matrix(self.train_set)\n",
    "            weighted_loss, w = self.eval()\n",
    "            if weighted_loss < best_loss:\n",
    "                best_loss = weighted_loss\n",
    "                best_w = w\n",
    "\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                difficulties = outputs[seq_lens-1, torch.arange(real_batch_size), 1]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                r_m, count = self.model.query_r_matrix(stabilities, difficulties, retentions)\n",
    "                weight = self.model.w[13] * torch.pow(torch.tensor(count / 300), self.model.w[14])\n",
    "                r_w = (weight * r_m + (1 - weight) * retentions).clamp(0.0001, 0.9999)\n",
    "                loss = self.loss_fn(r_w, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if verbose and (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    tqdm.write(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        tqdm.write(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        return best_w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            difficulties = outputs[seq_lens-1, torch.arange(real_batch_size), 1]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            r_m, count = self.model.query_r_matrix(stabilities, difficulties, retentions)\n",
    "            weight = self.model.w[13] * torch.pow(torch.tensor(count / 300), self.model.w[14])\n",
    "            r_w = (weight * r_m + (1 - weight) * retentions).clamp(0.0001, 0.9999)\n",
    "            tran_loss = self.loss_fn(r_w, labels)/len(self.train_set)\n",
    "            self.avg_train_losses.append(tran_loss)\n",
    "            tqdm.write(f\"Loss in trainset: {tran_loss:.4f}\")\n",
    "\n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            difficulties = outputs[seq_lens-1, torch.arange(real_batch_size), 1]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            r_m, count = self.model.query_r_matrix(stabilities, difficulties, retentions)\n",
    "            weight = self.model.w[13] * torch.pow(torch.tensor(count / 300), self.model.w[14])\n",
    "            r_w = (weight * r_m + (1 - weight) * retentions).clamp(0.0001, 0.9999)\n",
    "            tran_loss = self.loss_fn(r_w, labels)/len(self.train_set)\n",
    "            test_loss = self.loss_fn(retentions, labels)/len(self.test_set)\n",
    "            self.avg_eval_losses.append(test_loss)\n",
    "            tqdm.write(f\"Loss in testset: {test_loss:.4f}\")\n",
    "\n",
    "            w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "\n",
    "            weighted_loss = (tran_loss * len(self.train_set) + test_loss * len(self.test_set)) / (len(self.train_set) + len(self.test_set))\n",
    "\n",
    "            return weighted_loss, w\n",
    "\n",
    "    def plot(self):\n",
    "        fig = plt.figure()\n",
    "        ax = fig.gca()\n",
    "        ax.plot(self.avg_train_losses, label='train')\n",
    "        ax.plot(self.avg_eval_losses, label='test')\n",
    "        ax.set_xlabel('epoch')\n",
    "        ax.set_ylabel('loss')\n",
    "        ax.legend()\n",
    "        return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94c5b19d21194a298f543bf9b7ce1ffb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/88151 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n",
      "TRAIN: 57372 TEST: 30779\n",
      "dataset built\n",
      "Loss in trainset: 0.3503\n",
      "Loss in testset: 0.3242\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f05f387cca84ea08cf9f0d0b1ee3342",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/15276 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.2948, 1.8622, 5.0, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2.0, -0.2, 0.2, 1.0, 0.99, 0.4876]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a94b4fa4dd84fb08aa0c012df15eaef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/156840 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3174\n",
      "Loss in testset: 0.3245\n",
      "iteration: 15360\n",
      "w: [1.377, 2.0663, 4.9823, -1.4729, -1.1615, 0.05, 1.6365, -0.7283, 1.0117, 2.0985, -0.1287, 0.2306, 1.1246, 0.99, 0.2763]\n",
      "iteration: 30720\n",
      "w: [1.3814, 2.0773, 4.7181, -1.9039, -1.3882, 0.05, 2.0, -0.8, 1.3812, 2.0317, -0.2594, 0.1751, 1.1138, 0.9698, 0.3328]\n",
      "iteration: 46080\n",
      "w: [1.3816, 2.0778, 4.5135, -2.518, -1.5051, 0.05, 2.0, -0.7579, 1.5, 2.068, -0.2368, 0.2314, 1.1408, 0.8648, 0.3659]\n",
      "Loss in trainset: 0.3167\n",
      "Loss in testset: 0.3137\n",
      "iteration: 60984\n",
      "w: [1.3816, 2.0778, 4.2384, -2.6022, -1.4414, 0.05, 2.0, -0.7294, 1.5, 2.0352, -0.2939, 0.2767, 1.1128, 0.9615, 0.2488]\n",
      "iteration: 76344\n",
      "w: [1.3816, 2.0778, 4.1579, -2.5684, -1.4621, 0.0507, 2.0, -0.8, 1.5, 1.9956, -0.3311, 0.2114, 1.1153, 0.9631, 0.3144]\n",
      "iteration: 91704\n",
      "w: [1.3816, 2.0778, 4.0783, -2.5809, -1.4674, 0.05, 2.0, -0.8, 1.5, 2.0412, -0.292, 0.2578, 1.1684, 0.9891, 0.2562]\n",
      "Loss in trainset: 0.3119\n",
      "Loss in testset: 0.3139\n",
      "iteration: 106608\n",
      "w: [1.3816, 2.0778, 4.0399, -2.6256, -1.458, 0.05, 2.0, -0.8, 1.4994, 2.0589, -0.2749, 0.2811, 1.1683, 0.972, 0.2962]\n",
      "iteration: 121968\n",
      "w: [1.3816, 2.0778, 4.0297, -2.6326, -1.4547, 0.0609, 2.0, -0.7953, 1.5, 2.0537, -0.2795, 0.2699, 1.1684, 0.99, 0.2013]\n",
      "iteration: 137328\n",
      "w: [1.3816, 2.0778, 4.0245, -2.6397, -1.4517, 0.0619, 2.0, -0.7939, 1.5, 2.0458, -0.288, 0.2635, 1.1651, 0.9862, 0.1875]\n",
      "iteration: 152688\n",
      "w: [1.3816, 2.0778, 4.0238, -2.6392, -1.4525, 0.0597, 2.0, -0.7956, 1.5, 2.0468, -0.2869, 0.2636, 1.1667, 0.985, 0.1881]\n",
      "Loss in trainset: 0.3154\n",
      "Loss in testset: 0.3160\n",
      "TRAIN: 59696 TEST: 28455\n",
      "dataset built\n",
      "Loss in trainset: 0.3303\n",
      "Loss in testset: 0.3631\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df91fbeba07742e8abbea7ff3e8737b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/22374 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.8577, 1.8888, 5.0, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2.0, -0.2, 0.2, 1.0, 0.8207, 0.5015]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bef8e9b966484dca8c12b1ff61dd81a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/156714 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3070\n",
      "Loss in testset: 0.3697\n",
      "iteration: 15360\n",
      "w: [1.9545, 2.0051, 4.6467, -1.5034, -1.1427, 0.05, 1.9578, -0.5079, 1.2939, 2.0347, -0.1673, 0.1663, 1.0797, 0.9147, 0.4175]\n",
      "iteration: 30720\n",
      "w: [1.9594, 2.0111, 4.6754, -2.39, -1.7059, 0.05, 1.9978, -0.8, 1.4311, 2.0637, -0.18, 0.3189, 1.1765, 0.8206, 0.4252]\n",
      "iteration: 46080\n",
      "w: [1.9596, 2.0113, 4.0844, -2.5437, -1.6477, 0.05, 2.0, -0.7534, 1.5, 2.0438, -0.244, 0.2725, 1.1526, 0.7671, 0.4284]\n",
      "Loss in trainset: 0.3045\n",
      "Loss in testset: 0.3473\n",
      "iteration: 60942\n",
      "w: [1.9597, 2.0113, 3.7835, -2.4494, -1.6936, 0.0508, 2.0, -0.7995, 1.5, 2.118, -0.1904, 0.2652, 1.2035, 0.8985, 0.3331]\n",
      "iteration: 76302\n",
      "w: [1.9597, 2.0113, 3.6837, -2.5078, -1.6419, 0.0562, 2.0, -0.7462, 1.5, 2.0982, -0.2139, 0.2448, 1.1996, 0.8848, 0.2965]\n",
      "iteration: 91662\n",
      "w: [1.9597, 2.0113, 3.5863, -2.5354, -1.572, 0.0608, 2.0, -0.7143, 1.5, 2.0631, -0.2479, 0.1993, 1.1673, 0.9243, 0.273]\n",
      "Loss in trainset: 0.2992\n",
      "Loss in testset: 0.3485\n",
      "iteration: 106524\n",
      "w: [1.9597, 2.0113, 3.519, -2.5637, -1.5641, 0.05, 2.0, -0.7276, 1.5, 2.0604, -0.2511, 0.1895, 1.1641, 0.9194, 0.2505]\n",
      "iteration: 121884\n",
      "w: [1.9597, 2.0113, 3.5145, -2.5864, -1.5546, 0.05, 2.0, -0.7542, 1.5, 2.0801, -0.2319, 0.212, 1.1935, 0.9685, 0.221]\n",
      "iteration: 137244\n",
      "w: [1.9597, 2.0113, 3.5081, -2.5895, -1.5578, 0.0517, 2.0, -0.7515, 1.5, 2.0808, -0.2332, 0.2102, 1.1965, 0.9836, 0.2184]\n",
      "iteration: 152604\n",
      "w: [1.9597, 2.0113, 3.5069, -2.5898, -1.5577, 0.0534, 2.0, -0.7509, 1.5, 2.0814, -0.2326, 0.2096, 1.1964, 0.9875, 0.217]\n",
      "Loss in trainset: 0.3027\n",
      "Loss in testset: 0.3495\n",
      "TRAIN: 59234 TEST: 28917\n",
      "dataset built\n",
      "Loss in trainset: 0.3421\n",
      "Loss in testset: 0.3386\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65783bbaf6c64548a827cb0e36c0778e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/20916 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0856, 1.5972, 5.0, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2.0, -0.2, 0.2, 1.0, 0.99, 0.4338]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0bd53b500667478f9933b47fce8c857b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/156786 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3092\n",
      "Loss in testset: 0.3383\n",
      "iteration: 15360\n",
      "w: [1.0401, 1.6982, 4.6186, -1.4826, -1.0208, 0.05, 1.946, -0.8, 1.3027, 2.0603, -0.1226, 0.1924, 1.0025, 0.99, 0.2941]\n",
      "iteration: 30720\n",
      "w: [1.0377, 1.7035, 4.2483, -1.9312, -1.236, 0.05, 2.0, -0.8, 1.5, 1.8937, -0.3286, 0.1367, 0.8081, 0.8341, 0.5125]\n",
      "iteration: 46080\n",
      "w: [1.0376, 1.7037, 3.7626, -2.5836, -1.3542, 0.05, 2.0, -0.7216, 1.5, 1.9703, -0.3204, 0.1966, 0.9135, 0.8028, 0.5101]\n",
      "Loss in trainset: 0.3088\n",
      "Loss in testset: 0.3244\n",
      "iteration: 60966\n",
      "w: [1.0376, 1.7037, 3.4984, -2.9052, -1.3789, 0.0505, 2.0, -0.7899, 1.5, 2.1527, -0.1934, 0.3429, 0.9981, 0.99, 0.2303]\n",
      "iteration: 76326\n",
      "w: [1.0376, 1.7037, 3.304, -2.9472, -1.3732, 0.0642, 2.0, -0.8, 1.5, 2.0448, -0.3121, 0.171, 0.9091, 0.9697, 0.3669]\n",
      "iteration: 91686\n",
      "w: [1.0376, 1.7037, 3.1633, -3.0228, -1.3686, 0.0739, 2.0, -0.8, 1.5, 2.0967, -0.2759, 0.2512, 0.9861, 0.972, 0.2873]\n",
      "Loss in trainset: 0.3084\n",
      "Loss in testset: 0.3275\n",
      "iteration: 106572\n",
      "w: [1.0376, 1.7037, 3.0436, -3.0556, -1.3961, 0.0821, 2.0, -0.8, 1.5, 2.0917, -0.2885, 0.2402, 0.9937, 0.8723, 0.3786]\n",
      "iteration: 121932\n",
      "w: [1.0376, 1.7037, 3.0158, -3.0512, -1.3815, 0.1058, 2.0, -0.7994, 1.5, 2.1198, -0.2583, 0.2533, 1.0098, 0.9562, 0.2503]\n",
      "iteration: 137292\n",
      "w: [1.0376, 1.7037, 3.0112, -3.056, -1.3897, 0.0985, 2.0, -0.7992, 1.5, 2.117, -0.2632, 0.248, 1.0013, 0.9342, 0.2291]\n",
      "iteration: 152652\n",
      "w: [1.0376, 1.7037, 3.0102, -3.0565, -1.3901, 0.0992, 2.0, -0.8, 1.5, 2.1201, -0.2603, 0.2499, 1.0045, 0.9365, 0.2232]\n",
      "Loss in trainset: 0.3114\n",
      "Loss in testset: 0.3302\n",
      "\n",
      "Training finished!\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAGwCAYAAABSN5pGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABkjUlEQVR4nO3deXxU1f3/8dfMJJN9IQnZIBAgyE4iBCJCFSWIgrHuSHFDi/arWDW2VWq/avVro79WxQW11VotWrEqtQKKQgAVDIsgq+w7ZCFhSUgC2eb+/hgyMCFACEnuTPJ+Ph7zcDJz5s7nZoh555xzz7EYhmEgIiIiIi5WswsQERER8TQKSCIiIiJ1KCCJiIiI1KGAJCIiIlKHApKIiIhIHQpIIiIiInUoIImIiIjU4WN2Ad7K4XCQm5tLSEgIFovF7HJERESkAQzD4MiRI8THx2O1nr6fSAGpkXJzc0lISDC7DBEREWmEPXv20LFjx9M+r4DUSCEhIYDzGxwaGmpyNSIiItIQJSUlJCQkuH6Pn44CUiPVDquFhoYqIImIiHiZs02P0SRtERERkToUkERERETqUEASERERqUNzkERERDxMTU0NVVVVZpfhlXx9fbHZbOd9HAUkERERD2EYBvn5+Rw+fNjsUrxaeHg4sbGx57VOoQKSiIiIh6gNR9HR0QQGBmoh4nNkGAbl5eXs378fgLi4uEYfSwFJRETEA9TU1LjCUWRkpNnleK2AgAAA9u/fT3R0dKOH2zRJW0RExAPUzjkKDAw0uRLvV/s9PJ95XApIIiIiHkTDauevKb6HCkgiIiIidSggiYiIiNShgCQiIiIeIzExkSlTpphdhq5i8zQOh8HynQcZ0LkdvjblVxER8XzDhw8nJSWlSYLN8uXLCQoKOv+izpN+A3uYa6YuYuzflvD9tgNmlyIiItIkDMOgurq6QW3bt2/vEVfyKSB5mAsT2gEwc3WuyZWIiIjZDMOgvLLalJthGA2q8c477+Sbb77h5ZdfxmKxYLFYePfdd7FYLHz55ZcMHDgQPz8/Fi1axLZt2/j5z39OTEwMwcHBDBo0iHnz5rkdr+4Qm8Vi4e233+a6664jMDCQ7t278/nnnzflt7leGmLzMBnJ8Uxbsouv1uXz7HV98fM5//1kRETEOx2tqqH3E1+Z8t4/PT2KQPvZY8LLL7/M5s2b6du3L08//TQA69evB+Cxxx7jL3/5C127dqVdu3bs2bOH0aNH8+yzz+Ln58c///lPMjIy2LRpE506dTrte/zxj3/k//2//8ef//xnXn31VcaPH8+uXbuIiIhompOth3qQPExq53bEhvpzpKKabzYVml2OiIjIGYWFhWG32wkMDCQ2NpbY2FjX6tVPP/00I0eOpFu3bkRERJCcnMy9995L37596d69O8888wzdunU7a4/QnXfeybhx40hKSuJPf/oTpaWlLFu2rFnPSz1IHsZqtXB1/zjeXrSDmWvyuKJPrNkliYiISQJ8bfz09CjT3vt8paamun1dWlrKU089xezZs8nLy6O6upqjR4+ye/fuMx6nf//+rvtBQUGEhoa69ltrLgpIHigjOZ63F+1g3k8FlFdWN6iLU0REWh+LxeLVvwPqXo32m9/8hrlz5/KXv/yFpKQkAgICuPHGG6msrDzjcXx9fd2+tlgsOByOJq/3ZBpi80D9O4bRKSKQo1U1ZG9o3oQsIiJyvux2OzU1NWdtt3jxYu68806uu+46+vXrR2xsLDt37mz+AhtBAckDWSwWMpLjAF3NJiIini8xMZGlS5eyc+dOioqKTtu70717d2bMmMGqVatYvXo1v/jFL5q9J6ixFJA8VEZyPAALNxVScqzxuxGLiIg0t9/85jfYbDZ69+5N+/btTzun6MUXX6Rdu3ZcfPHFZGRkMGrUKAYMGNDC1TaMxWjoQgfipqSkhLCwMIqLiwkNDW2W97jipW/YXFDKX25K5saBHZvlPURExDMcO3aMHTt20KVLF/z9/c0ux6ud6XvZ0N/f6kHyYBn9nb1IGmYTERFpWQpIHuzq48Nsi7YWcbDszDP8RUREpOkoIHmwLlFB9OsQRo3D4Iu1eWaXIyIi0mYoIHk4Xc0mIiLS8hSQPNyY4/OQlu08SH7xMZOrERERaRsUkDxch/AAUju3wzBgtobZREREWoQCkheoXRNJw2wiIiItQwHJC1zVLxarBVbtOcyeg+VmlyMiItLqeURAmjp1KomJifj7+5OWlsayZctO23bGjBmkpqYSHh5OUFAQKSkpTJs27bTtf/WrX2GxWJgyZYrb4wcPHmT8+PGEhoYSHh7O3XffTWlpaVOdUpOKDvFnSLdIAGauUS+SiIhIczM9IH300UdkZmby5JNPsnLlSpKTkxk1ahT799e/SWtERASPP/44OTk5rFmzhgkTJjBhwgS++uqrU9r+5z//YcmSJcTHx5/y3Pjx41m/fj1z585l1qxZfPvtt9xzzz1Nfn5N5cSikZqHJCIinmX48OE89NBDTXa8O++8k2uvvbbJjtcYpgekF198kYkTJzJhwgR69+7Nm2++SWBgIO+880697YcPH851111Hr1696NatGw8++CD9+/dn0aJFbu327dvHAw88wAcffICvr6/bcxs2bGDOnDm8/fbbpKWlMWzYMF599VWmT59Obm79PTQVFRWUlJS43VrSlX1j8bFa2JBXwtb9R1r0vUVERNoaUwNSZWUlK1asID093fWY1WolPT2dnJycs77eMAyys7PZtGkTl1xyietxh8PBbbfdxm9/+1v69OlzyutycnIIDw8nNTXV9Vh6ejpWq5WlS5fW+15ZWVmEhYW5bgkJCedyquctPNDOJRe0B9SLJCIinuPOO+/km2++4eWXX8ZisWCxWNi5cyfr1q3jqquuIjg4mJiYGG677TaKiopcr/vkk0/o168fAQEBREZGkp6eTllZGU899RTvvfce//3vf13HW7hwYYufl6kBqaioiJqaGmJiYtwej4mJIT8//7SvKy4uJjg4GLvdzpgxY3j11VcZOXKk6/nnn38eHx8ffv3rX9f7+vz8fKKjo90e8/HxISIi4rTvO3nyZIqLi123PXv2NPQ0m4xr0cg1uWiPYRGRNsAwoLLMnFsDf8+8/PLLDBkyhIkTJ5KXl0deXh4hISFcfvnlXHjhhfzwww/MmTOHgoICbr75ZgDy8vIYN24cd911Fxs2bGDhwoVcf/31GIbBb37zG26++WauvPJK1/Euvvji5vwu18unxd+xCYSEhLBq1SpKS0vJzs4mMzOTrl27Mnz4cFasWMHLL7/MypUrsVgsTfaefn5++Pn5NdnxGiO9Vwx+Pla2F5bxU14JfeLDTK1HRESaWVU5/OnUebQt4ve5YA86a7OwsDDsdjuBgYHExsYC8H//939ceOGF/OlPf3K1e+edd0hISGDz5s2UlpZSXV3N9ddfT+fOnQHo16+fq21AQAAVFRWu45nB1B6kqKgobDYbBQUFbo8XFBSc8ZtitVpJSkoiJSWFRx55hBtvvJGsrCwAvvvuO/bv30+nTp3w8fHBx8eHXbt28cgjj5CYmAhAbGzsKZPAq6urOXjwoKkfxtmE+PtyeU9nz5eG2URExFOtXr2aBQsWEBwc7Lr17NkTgG3btpGcnMyIESPo168fN910E2+99RaHDh0yuWp3pvYg2e12Bg4cSHZ2tmu2usPhIDs7m0mTJjX4OA6Hg4qKCgBuu+02tzlNAKNGjeK2225jwoQJAAwZMoTDhw+zYsUKBg4cCMD8+fNxOBykpaU1wZk1n4zkeL5cl8/M1bk8emWPJu0lExERD+Mb6OzJMeu9G6m0tJSMjAyef/75U56Li4vDZrMxd+5cvv/+e77++mteffVVHn/8cZYuXUqXLl3Op+omY/oQW2ZmJnfccQepqakMHjyYKVOmUFZW5gozt99+Ox06dHD1EGVlZZGamkq3bt2oqKjgiy++YNq0abzxxhsAREZGEhkZ6fYevr6+xMbG0qNHDwB69erFlVdeycSJE3nzzTepqqpi0qRJ3HLLLfUuCeBJLusRTZDdxr7DR/lxz2EGdGpndkkiItJcLJYGDXOZzW63U1NT4/p6wIABfPrppyQmJuLjU3/UsFgsDB06lKFDh/LEE0/QuXNn/vOf/5CZmXnK8cxgekAaO3YshYWFPPHEE+Tn55OSksKcOXNcE7d3796N1XpiJLCsrIz77ruPvXv3EhAQQM+ePXn//fcZO3bsOb3vBx98wKRJkxgxYgRWq5UbbriBV155pUnPrTkE2G2M7B3DZ6tymbk6VwFJRERMl5iYyNKlS9m5cyfBwcHcf//9vPXWW4wbN47f/e53REREsHXrVqZPn87bb7/NDz/8QHZ2NldccQXR0dEsXbqUwsJCevXq5TreV199xaZNm4iMjCQsLOyUJXuam8XQ5VCNUlJSQlhYGMXFxYSGhrboe2dvKODu934gOsSPnMkjsFk1zCYi4u2OHTvGjh076NKlC/7+/maXc042b97MHXfcwerVqzl69Cg7duygqqqKRx99lAULFlBRUUHnzp258sorefHFF9m4cSMPP/wwK1eupKSkhM6dO/PAAw+4ptcUFhYyfvx4cnJyKC0tZcGCBQwfPrzB9Zzpe9nQ398KSI1kZkCqrHaQ+n9zKTlWzYcTL3JtQyIiIt7LmwOSp2mKgGT6Stpy7uw+Vq7qe2JNJBEREWlaCkhe6poU52TyL9fmUVXjMLkaERGR1kUByUtd1DWSqGA/DpVXsXhr0dlfICIiIg2mgOSlbFYLY/o5F7XUopEiIiJNy/TL/KWOvT/A0UNgs4OPn/O/J98/6bFr+rXnvZydfL0+n2NVffH3tZldvYiInCddO3X+muJ7qIDkaRZmwdZ5DWo6ENjub6HS8MH2vB/Y/esEKV+w+dUJWvZGPFZ7nPoeO837WRXWRETORe06P+Xl5QQEBJhcjXcrLy8HOK+1kxSQPE1EV4hLhupKqKk48d+ayhP3jROTsq0Y+FuqoLoKqktNLLwOi62Rgez4126BrL7H6glupxynzmM2O1g1qiwinslmsxEeHu7aKzQwMFDbSZ0jwzAoLy9n//79hIeHY7M1/o91BSRPM/rPZ29TU+0MTDUVbNhTxC/fWUyIbw0z7h1MoLW6Trg6Q9A678eO36ornI+dzKiB6qPOmyex+jYykNXzmM33eFCr57H4CyGso9lnKyJepnbD9Lobqsu5CQ8PP+/N5xWQvJHNx3kjkJ7dw/GN3MvGA+XMLYrg5ykdzKnJMKCm6tTQVG9Iq6rz/Pk8drr3O/6Yo8q9TkeV81ZV1rzfD5sf3DkLEgY37/uISKtisViIi4sjOjqaqqqqs79ATuHr63tePUe1FJC8nMViISM5nlfnb2Xm6jzzApLF4uxF8bGb8/6n43CcCEwnBylX0GroYw0MZDWVUJILB7fB9PFwz0IIM+kzERGvZbPZmuSXvDSeAlIrUBuQvtm8n+LyKsICW3ZDP49mtYLVH3xbcNn+ilJ4ZxQUrIPpv4C75oCvJlyKiHgTzVhtBS6ICaFHTAhVNQZf/ZRvdjniFwy3fAABEZC3Cj5/wDkEKSIiXkMBqZXISD6+N9tq7c3mEdolws3/BKsPrP0YFr9sdkUiInIOFJBaiav7O/dm+37bAYpKK87SWlpEl5/Blc857897CjZ/bWo5IiLScApIrURiVBD9O4ZR4zD4cp2G2TzGoF/CwDsBAz69Gwo3m12RiIg0gAJSK5JxvBdJw2wexGKBq/4MnS6GihKYPg6OHja7KhEROQsFpFZkTH/nPKTlOw+SV+xhCzS2ZT5253yksAQ4sNXZk+SoMbsqERE5AwWkViQ+PIBBie0wDJi9Js/scuRkwe3hln+BT4Bzr715T5pdkYiInIECUiuTkXx8mE0ByfPE9YdrX3fe//5VWD3d3HpEROS0FJBamav6xmG1wOo9h9l9oNzscqSuvtfDz37jvP/5r2HvCnPrERGReikgtTLtQ/y4uFsUADPXaLK2R7rscegx2rlVyUfj4YiuOhQR8TQKSK2QFo30cFYrXPdXaN8TjuQ592yrOmZ2VSIichIFpFboyj5x+NosbMw/wpaCI2aXI/XxD4VxH4J/OOz7AWY9rO1IREQ8iAJSKxQW6MulF7QHNFnbo0V0hZveBYsNVv8LlrxudkUiInKcAlIrVXs126zVuRjqmfBc3S6DUc8673/9B9iabW49IiICKCC1Wum9YvD3tbK9qIz1uSVmlyNnkvYrSLkVDAd8MgEObDO7IhGRNk8BqZUK8vNhRM8YQFezeTyLBa5+EToOhmPF8OE4OKZQKyJiJgWkVqz2arZZq/NwODTM5tF8/GDs+xDaAYo2wYyJ2o5ERMRECkit2PAe0QT7+bDv8FF+3HPI7HLkbEJi4JYPwMcfNs+B+f9ndkUiIm2WAlIr5u9r44rex4fZVutqNq8QfyH8fKrz/qIXYe0n5tYjItJGKSC1cq6r2dbkUaNhNu/Q70YY+pDz/n8nQe6PppYjItIWKSC1ckOToggP9KWotIKl2w+YXY401IgnoPsoqD7qXGn7SIHZFYmItCkKSK2c3cfKVX1jAV3N5lWsNrjhLYi6AEr2wb9vg+oKs6sSEWkzFJDagIz+zmG2L9flU1ntMLkaaTD/MLjlQ/ALgz1LYfYj2o5ERKSFKCC1AWldI4kK9uNweRWLtxaZXY6ci6gkuOkdsFjhx2mw7G9mVyQi0iYoILUBNquFq/s710SauVrDbF4nKR1GPu28P2cybF9oajkiIm2BAlIbUbto5Nc/FXCsSgsQep0hk6D/LWDUwMd3wsEdZlckItKqKSC1ERcmtKNDeAClFdUs3LTf7HLkXFkskPEydBgIRw85tyOpOGJ2VSIirZZHBKSpU6eSmJiIv78/aWlpLFu27LRtZ8yYQWpqKuHh4QQFBZGSksK0adPc2jz11FP07NmToKAg2rVrR3p6OkuXLnVrk5iYiMVicbs999xzzXJ+nsDqNsymRSO9kq8/jP0AgmOhcAPMuBccmnQvItIcTA9IH330EZmZmTz55JOsXLmS5ORkRo0axf799fdyRERE8Pjjj5OTk8OaNWuYMGECEyZM4KuvvnK1ueCCC3jttddYu3YtixYtIjExkSuuuILCwkK3Yz399NPk5eW5bg888ECznqvZaheNzN5YQGlFtcnVSKOExjm3I7H5wabZsDDL7IpERFoli2GYe91wWloagwYN4rXXXgPA4XCQkJDAAw88wGOPPdagYwwYMIAxY8bwzDPP1Pt8SUkJYWFhzJs3jxEjRgDOHqSHHnqIhx56qEHvUVFRQUXFiXVoSkpKSEhIoLi4mNDQ0AYdw2yGYXD5C9+wo6iMl29J4ecpHcwuSRpr1Yfw2a+c9296F/pcZ2o5IiLeojYTnO33t6k9SJWVlaxYsYL09HTXY1arlfT0dHJycs76esMwyM7OZtOmTVxyySWnfY+//e1vhIWFkZyc7Pbcc889R2RkJBdeeCF//vOfqa4+fa9KVlYWYWFhrltCQkIDz9JzWCwWMnQ1W+uQMs45cRvgs/sgb4259YiItDKmBqSioiJqamqIiYlxezwmJob8/PzTvq64uJjg4GDsdjtjxozh1VdfZeTIkW5tZs2aRXBwMP7+/rz00kvMnTuXqKgo1/O//vWvmT59OgsWLODee+/lT3/6E7/73e9O+56TJ0+muLjYdduzZ08jz9pctcNs32wupLi8yuRq5Lyk/xG6XQ5V5c7tSMq0xpWISFPxMbuAxggJCWHVqlWUlpaSnZ1NZmYmXbt2Zfjw4a42l112GatWraKoqIi33nqLm2++maVLlxIdHQ1AZmamq23//v2x2+3ce++9ZGVl4efnd8p7+vn51fu4t+keE0LP2BA25h/hq/X53DzI+3rC5DibD9z4Drw1Ag5ug3/fDrd9Bj52sysTEfF6pvYgRUVFYbPZKChw34izoKCA2NjY077OarWSlJRESkoKjzzyCDfeeCNZWe6TVYOCgkhKSuKiiy7i73//Oz4+Pvz9738/7THT0tKorq5m586d53VO3qC2F0l7s7UCAe1g3IdgD4Fdi2HOo2ZXJCLSKpgakOx2OwMHDiQ7O9v1mMPhIDs7myFDhjT4OA6Hw20CdWParFq1CqvV6uphas1q92ZbvLWIolJtgOr12veAG/8OWOCHd2D56f8QEBGRhjF9iC0zM5M77riD1NRUBg8ezJQpUygrK2PChAkA3H777XTo0MHVQ5SVlUVqairdunWjoqKCL774gmnTpvHGG28AUFZWxrPPPss111xDXFwcRUVFTJ06lX379nHTTTcBkJOTw9KlS7nssssICQkhJyeHhx9+mFtvvZV27dqZ841oQZ0iA0lOCGf1nsN8uTaP24Ykml2SnK8LRsGIJyD7j/Dl75yhKXGY2VWJiHgt0wPS2LFjKSws5IknniA/P5+UlBTmzJnjmri9e/durNYTHV1lZWXcd9997N27l4CAAHr27Mn777/P2LFjAbDZbGzcuJH33nuPoqIiIiMjGTRoEN999x19+vQBnPOJpk+fzlNPPUVFRQVdunTh4YcfdpuX1Npl9I9j9Z7DzFytgNRqDHsYCtbDuk+c85EmLoB2nc2uSkTEK5m+DpK3aug6Cp4qv/gYQ57LxjAgZ/LlxIUFmF2SNIXKcvjHlZC3GmL6wt1fgz3I7KpERDyGV6yDJOaJDfNnUGIEALO09UjrYQ+EW/4FQdFQsA4++x/Q30AiIudMAakN09VsrVRYRxg7Day+8NN/4ds/m12RiIjXUUBqw67qG4vNamHN3mJ2FpWZXY40pU4XwdUvOu8veBY2zDK3HhERL6OA1IZFBftxcbdIAGapF6n1GXA7DL7Xef8/90LBT+bWIyLiRRSQ2jjXMJvmIbVOo56FLpdAZSl8eAuUHzS7IhERr6CA1MaN6hOLr83CpoIjbMo/YnY50tRsvnDTe9AuEQ7vcl7+X6M9+EREzkYBqY0LC/Dl0gucq4drmK2VCoyAcdPBHgw7v4Ovfm92RSIiHk8BSchIjgNg5upctCxWKxXdC67/m/P+sr/BivfMrUdExMMpIAnpvWLw97Wy80A56/aVmF2ONJeeY+CyPzjvz34EduWYW4+IiAdTQBKC/HwY0cu5tYvWRGrlLvkN9L4WHFXw79vg8B6zKxIR8UgKSAJARn/n1WyzVuficGiYrdWyWODa1yG2H5QVwvRfOLcnERERNwpIAsDwHu0J9vMht/gYK3cfMrscaU72IOd2JIFRkL8G/nu/tiMREalDAUkA8Pe1cUWf48NsqzXM1uqFdzq+HYkPrJ8Bi140uyIREY+igCQutYtGzl6bR3WNw+RqpNl1vhhGH9+nLfsZ2PSlufWIiHgQBSRxGZYURXigL0WllSzdoRWX24TUuyD1bsCATyfC/o1mVyQi4hEUkMTF12blqr4n1kSSNuKq56HzMKg8AtPHwVHNQRMRUUASN9ccH2b7cl0+ldUaZmsTbL5w83sQ1gkOboePJ0BNtdlViYiYSgFJ3AzuEkF0iB/FR6tYtLXQ7HKkpQRFwbh/gW8gbF8Ac58wuyIREVMpIIkbm9XCmP61w2x5JlcjLSq2H1z3pvP+kqmw6l/m1iMiYiIFJDlF7dVsX6/P51hVjcnVSIvq/XO49FHn/ZkPwp7l5tYjImISBSQ5xYUJ4XQID6CssoYFG/ebXY60tEsfg55XQ00lfDQeSjRhX0TaHgUkOYXFYnH1ImlvtjbIaoXr/grRvaG0AKaPh6qjZlclItKiFJCkXhnJznlI2Rv2c+RYlcnVSIvzC3ZuRxLQDnJXOofbtB2JiLQhCkhSr95xoXRtH0RFtYN5GwrMLkfMENEFbnoPLDZY8xF8/6rZFYmItBgFJKmXxWIho//xYTZdzdZ2db0UrnzOeX/ek7Blnrn1iIi0EAUkOa3aYbZvNxdyuLzS5GrENIMnwoDbwXDAJ3dB0RazKxIRaXYKSHJaSdEh9IoLpdphMGddvtnliFksFhj9AiRcBBXF8OE4OFZsdlUiIs1KAUnOqLYXSVeztXE+dhg7DUI7woEt8Mnd4NAaWSLSeikgyRnVzkPK2XaA/UeOmVyNmCo4Gm75AHwCYOtcyP6j2RWJiDQbBSQ5o4SIQFISwnEY8OVaDbO1efEpcO1U5/3FL8Oaf5tajohIc1FAkrNyLRq5WsNsAvS9AYZlOu//dxLsW2FuPSIizUABSc5qTL84LBb4Ydch9h3WisoCXP6/cMFVUFPhXGn7iHoXRaR1UUCSs4oN82dwYgQAszVZW8C5Hcn1f4P2PeFIHnx0K1RpjpqItB4KSNIgJ4bZtGikHOcf6tyOxD8c9i6H2ZnajkREWg0FJGmQq/rGYrNaWLuvmB1FZWaXI54ishvc9A+wWGHVB7DkDbMrEhFpEgpI0iCRwX4MTYoCYJYma8vJul0OVzzrvP/147Btvrn1iIg0AQUkabCM/lo0Uk7jov+BlPHO7Ug+ngAHtpldkYjIeVFAkga7ok8sdpuVzQWlbMo/YnY54kksFrj6Jeg4CI4dPr4dSYnZVYmINJoCkjRYWIAvw3u0B7QmktTDxw/Gvg8h8VC0CWbcAw6H2VWJiDSKRwSkqVOnkpiYiL+/P2lpaSxbtuy0bWfMmEFqairh4eEEBQWRkpLCtGnT3No89dRT9OzZk6CgINq1a0d6ejpLly51a3Pw4EHGjx9PaGgo4eHh3H333ZSWljbL+bUmrqvZ1uRi6IolqSskFm55H2x+sPlLWPB/ZlckIt7C4YCC9bD8bfj0l/BSXzi0y7RyfEx75+M++ugjMjMzefPNN0lLS2PKlCmMGjWKTZs2ER0dfUr7iIgIHn/8cXr27IndbmfWrFlMmDCB6OhoRo0aBcAFF1zAa6+9RteuXTl69CgvvfQSV1xxBVu3bqV9e2cPyPjx48nLy2Pu3LlUVVUxYcIE7rnnHv71r3+16Pl7mxG9ognwtbHrQDlr9xXTv2O42SWJp+kwEH7+GsyYCN+9ADF9nKtvi4icrOoY5K6E3TmwewnsWQrHit3b7F4C7TqbUp7FMLkbIC0tjUGDBvHaa68B4HA4SEhI4IEHHuCxxx5r0DEGDBjAmDFjeOaZZ+p9vqSkhLCwMObNm8eIESPYsGEDvXv3Zvny5aSmpgIwZ84cRo8ezd69e4mPjz/lGBUVFVRUVLgdMyEhgeLiYkJDQ8/1tL3aAx/+yMzVuUz8WRceH9Pb7HLEU339v/D9K87Nbe+a49zHTUTarvKDzhBUG4hyf4SaSvc2vkGQMAg6DYFOFznnNdqDmrSM2kxwtt/fpvYgVVZWsmLFCiZPnux6zGq1kp6eTk5OzllfbxgG8+fPZ9OmTTz//POnfY+//e1vhIWFkZycDEBOTg7h4eGucASQnp6O1Wpl6dKlXHfddaccJysriz/+UbuXg/Nqtpmrc5m1Jo/JV/XCarWYXZJ4ovSnYP8G2DrXuR3JPQsg+NReYRFphQwDDu9yBqHaQFS48dR2wTHOIFQbiGL6gc30wS3A5IBUVFRETU0NMTExbo/HxMSwcWM938jjiouL6dChAxUVFdhsNl5//XVGjhzp1mbWrFnccsstlJeXExcXx9y5c4mKcq7jk5+ff8rwnY+PDxEREeTn17+n1OTJk8nMzHR9XduD1BZd2qM9If4+5BUfY8XuQww6vg2JiBurDW54G95OhwNb4KPb4I6Z4GM3uzIRaWqOGihY5x6IjtSz80LUBe6BqF0X51WwHsgzYto5CgkJYdWqVZSWlpKdnU1mZiZdu3Zl+PDhrjaXXXYZq1atoqioiLfeeoubb76ZpUuX1juvqSH8/Pzw8/NrojPwbn4+Nkb1ieWTFXv5fFWuApKcXkA4jPsQ3hoBe5bAF7+BjJc99n+IItJAlWWwb8WJQLRnOVTWWf7F6uscWq8NRAlpEBRlSrmNYWpAioqKwmazUVBQ4PZ4QUEBsbGxp32d1WolKSkJgJSUFDZs2EBWVpZbQAoKCiIpKYmkpCQuuugiunfvzt///ncmT55MbGws+/fvdztmdXU1Bw8ePOP7ygkZyfF8smIvX6zN48mM3vjYPOKCSPFEUd3hxr/DBzfByvcgth8Mnmh2VSJyLkoLnX/k1AaivNXgqHZv4xcKCYNPBKL4AWAPNKfeJmBqQLLb7QwcOJDs7GyuvfZawDlJOzs7m0mTJjX4OA6Hw20C9dnaDBkyhMOHD7NixQoGDhwIwPz583E4HKSlpTXuZNqYi7tFEhFk50BZJTnbD/Cz7u3NLkk8WfeRMPKPMPcJ+PJRaN8DulxidlUiUh/DgIPbjw+VHR8uO7D11HYh8dB5yInhsujezqH1VsL0IbbMzEzuuOMOUlNTGTx4MFOmTKGsrIwJEyYAcPvtt9OhQweysrIA52Tp1NRUunXrRkVFBV988QXTpk3jjTecm2SWlZXx7LPPcs011xAXF0dRURFTp05l37593HTTTQD06tWLK6+8kokTJ/Lmm29SVVXFpEmTuOWWW+q9gk1O5WuzclXfWD5YupuZq3MVkOTsLv61c42TNR/Bv+9wTtpul2h2VSJSUwX5a9znD5UVntouurf7/KGwhFY9XG56QBo7diyFhYU88cQT5Ofnk5KSwpw5c1wTt3fv3o3VemL4pqysjPvuu4+9e/cSEBBAz549ef/99xk7diwANpuNjRs38t5771FUVERkZCSDBg3iu+++o0+fPq7jfPDBB0yaNIkRI0ZgtVq54YYbeOWVV1r25L1cRnI8HyzdzZx1+TxzbV/8fFrPXw7SDCwW5/yjos3Oy3s//AXc/TX4BZtdmUjbUnEE9i4/EYj2/gBV5e5tbHbnmma1gajjIAhsW/NNTV8HyVs1dB2F1qzGYXDxc9kUlFTw9u2ppPeOOfuLREpy4W/DobQAel4NN08Dq+awiTSbI/kneoZ250D+WufG0ifzDz8eho4HorgU8PU3o9pm5xXrIIl3s1ktjOkXzzuLdzBzTa4CkjRMaDyM/QDeHQ0bZ8E3z8Nlk8/+OhE5O8Nw9tKeHIgO7Ty1XXinE0NlnYZAVA/9oVKHApKcl4zkON5ZvIO5PxVwtLKGALuG2aQBEgbB1VPgv/fBN89BTG/o/XOzqxLxPtWVkLfqpEC0BI4erNPIArF9TwSihIsgrIMZ1XoVBSQ5LykJ4XRsF8DeQ0eZv3E/Y/rHmV2SeIsLxzsXllvyOvznVxDRzfk/cRE5vWPFzjWHaq8w27cCqo+5t/Hxd84Zqh0y6zgI/MPMqdeLKSDJebFYLGQkx/PGwm3MXJ2rgCTnZuQzzu1Iti+AD8c5r2zzooXkRJpd8T733qGCdUCdqcOBke7DZbH9tWJ9E1BAkvOW0d8ZkOZv2s+RY1WE+PuaXZJ4C5sP3PgOvHU5HNrhvPz/9s/Apn9D0gY5HM79yk4ORMW7T20X0dU9EEUmterL7c2igCTnrVdcCN3aB7GtsIy5PxVw/YCOZpck3iQwAsZNd+7ZtmuRcyHJq180uyqR5ld1zLnkRW0g2rPEOYR2MosN4vq7zx8K0QUxLUEBSc5b7TDblHlbmLk6VwFJzl10T7jhLecw2w9/d85FSr3L7KpEmlb5Qdiz7EQgyl0JNZXubXyDoGOqMxB1HgIdUrVWmEkUkKRJXN3fGZC+21LEobJK2gVp/FvOUY+r4PI/wPxn4IvfOi87ThxqdlUijWMYcHi3++rUhRtObRcU7b46dWw/DTF7CAUkaRJJ0cH0jgvlp7wS5qzPZ9zgTmaXJN7oZ484tyNZPwP+fRvcs9C5XouIp3PUOP/tnhyIjuSe2i6yu3sgiuiq+UMeSgFJmsw1KfH8lFfCzNW5CkjSOBYL/HwqHNzm3C38w1/A3V+BPcjsykTcVZY7L7GvDUR7lkHlEfc2Vh/nitQnByJdpek1FJCkyYzpF8dzX24kZ/sB9pccIzq0dS5TL83MHgi3/Mu5HUnBWvjsPrjpXf2VLeYqK3LvHcpbBY5q9zb2EEgYfCIMdRjo/PcsXkkBSZpMQkQgAzqFs3L3Yb5Ym8edQ7uYXZJ4q7COMPZ9ePdq+Okz+PYvcOlvza5K2grDgIPb3QPRgS2ntguJOx6GjgeimD5g1W4CrYUCkjSpjOR4Vu4+zMw1CkhynjpdBGNegJm/hgX/59yOpOcYs6uS1qimGvLXuAeisv2ntmvfy324LLyTejZbMQUkaVJj+sXx9KyfWLHrEHsPldOxnbqX5TwMvMO5cvCyv8GMe+Duuc6gJHI+Kkph7/ITgWjvD1BV5t7GZof4AScCUcJg55pd0mYoIEmTig7156IukeRsP8DsNXnce2k3s0sSbzfqT87tSHZ+B9PHwcQF+kUl5+ZI/omVqXfnQP5aMGrc2/iHORdhrA1E8ReCr+ZRtmUKSNLkMpLjydl+gJlrchWQ5PzZfOHmfzonbR/aCR/fCbfOcG5TIlKfQ7tg+8ITgejQjlPbhHU6sZlrpyHQvidYrS1eqngu/R9GmtyVfWN54r/rWLevhO2FpXRtr1Vg5TwFRsC4D+HtkbDjG/j6cbjqebOrEk9RUQo7F8G2bNia7Vwmwo0FYvqeFIgucl4IIHIGCkjS5CKC7AzrHsXCTYXMXJ3Hg+ndzS5JWoOYPnD9X+GjW2Hpm85feANuM7sqMYPD4ZxUvW2+87Z7CTiqTjxvsUHHQZA47Pj8oUHOITSRc6CAJM0io388CzcV8vnqffx6RBIWXekhTaFXBgz/PSz8E8x6GKIugE5pZlclLeFIwYlAtH0BlBW6Px/eGZJGQLcR0OVnCkRy3hSQpFmM7BOD/T9WthWWsTH/CL3iQs0uSVqLS37rvLJtw+fO3qR7Fmi4pDWqrnDOH9qaDdsWOBcNPZk9GBJ/djwUXa4tO6TJKSBJswj19+WyHu35an0BM1fnKiBJ07Fa4do3nAv5FayD6ePhrjngG2B2ZXI+DAOKtjjnEW2b75xTVFXu3iYuxRmGkkZAx8Hgo02xpfkoIEmzyUiOdwakNbn8dlQPDbNJ0/ELdm5H8tZlzi0f/jsJbnhbPQje5ugh2P7N8VC0AIr3uD8fHOMcMut2OXS7TPuYSYtSQJJmc3nPaALtNvYcPMrqvcWkJISbXZK0Ju06Oy///+fPYd0nENsXhj1sdlVyJjXVzg1et813hqJ9K8BwnHje5gedh5wIRTF9FHrFNApI0mwC7T6k94rh89W5zFydq4AkTS9xmPNy/9mPwLw/QnRvuGCU2VXJyQ7vPj6PaL5ziYZjxe7PR/U4Mbm688Xa3FU8hgKSNKuM5Hg+X53LrDW5PD66F1ar/hqUJjbol5C/Dlb8Az79JfxyHrTvYXZVbVdlmXP+UG0oqrvJq384dB1+YnK1JtiLh1JAkmZ1yQVRhPj7UFBSwfKdB0nrGml2SdIaXfX/oHAT7P4ePhwHE7MhoJ3ZVbUNDodzsnzt5OrdS6Cm8sTztWsS1U6ujr9QO96LV1BAkmbl52Pjyj6xfLxiLzPX5CogSfPwsTvnI711mXMV5U/uhvEf6xdxcynd75xUXTu5umy/+/PhnU7MI+pyCQSEm1KmyPlQQJJml5Ecz8cr9vLF2nyeyuiDj037HUkzCG7vvLLtnVHOX9xzn4BRz5pdVetQXeHsGaqdXJ1fZ00i3yDn4oy1oSiymyZXi9dTQJJmd3G3SCKC7Bwsq+T7bQe45IL2ZpckrVVcf7j2deeGtjmvObcjSRlndlXexzDgwNYT84h2fnfqmkSx/U9Mrk4YDD5+5tQq0kwUkKTZ+disjO4Xy/tLdjNzda4CkjSvPtdBwXr49s8w80GI6g4dU82uyvMdPey8yqx25eri3e7PB0WfmEfU9TJnj51IK6aAJC3imuQOvL9kN3PW5/N/1/XFz0dzQ6QZDf89FPwEm2Y7V9q+ZyGExpldlWdx1MC+lScmV+/9AYyaE8/b7M6NXmtDUUxfDZtJm9KogPTee+8RFRXFmDFjAPjd737H3/72N3r37s2HH35I586dm7RI8X6pndsRG+pPfskxvt1cxMjeMWaXJK2Z1QrX/xXeHgmFG+Cj8XDnF+Drb3Zl5jq858Q8ou0L61mT6IIT84gSh4I9yJQyRTyBxTAM41xf1KNHD9544w0uv/xycnJySE9P56WXXmLWrFn4+PgwY8aM5qjVo5SUlBAWFkZxcTGhodpnrCH+b9ZPvL1oB9ckx/PKuAvNLkfagoPb4a3LnVta9L8FrnuzbfWCVJbBzsUnQlHRZvfn/cOcaxLVhqLwBFPKFGlJDf393agepD179pCUlATAZ599xg033MA999zD0KFDGT58eKMKltYvIzmetxftYO5PBZRXVhNo1wivNLOIrnDTuzDtelgz3bkdycUPmF1V8zEM55pEtZOrd+fUWZPICh1ST0yu7jBASyGInEajfkMFBwdz4MABOnXqxNdff01mZiYA/v7+HD16tEkLlNajf8cwOkUEsvtgOfM37ufq/vFmlyRtQdfhMOpPMOdR56X/0b0gKd3sqppOaSFsX+AMRdsXQGmB+/NhnSDp8uNrEl2qNYlEGqhRAWnkyJH88pe/5MILL2Tz5s2MHj0agPXr15OYmNiU9UkrYrFYyEiOY+qCbcxcnauAJC0n7V5nz8qP0+Dju2DifIhKMruqxqmuhD1LT0yuzlvt/rxvICT+7MRWHpFJbWtYUaSJNCogTZ06lT/84Q/s2bOHTz/9lMhI5+rIK1asYNw4rTkip5eRHM/UBdtYsKmQkmNVhPr7ml2StAUWC4x5wTkHZ89S+PAW53Yk/mFmV3Z2hgEHtp2YR7TjO6gqc28T2885ZJY0AhLStCaRSBNo1CRt0STtxjIMgyte+pYt+0t54aZkbhiojSqlBZXuh78Nh5J90P0KGDfdM+fgHCuG7d+cCEWH665J1N7ZO9RtBHS7DIKjzalTxAs19Pd3o/Z8mDNnDosWLXJ9PXXqVFJSUvjFL37BoUOHzvl4U6dOJTExEX9/f9LS0li2bNlp286YMYPU1FTCw8MJCgoiJSWFadOmuZ6vqqri0UcfpV+/fgQFBREfH8/tt99Obm6u23ESExOxWCxut+eee+6ca5dz4xxmcw6tfb469yytRZpYcDTc8gH4+MOWryH7abMrcnLUONchWvg8/H0UPN8F/n0brPiHMxzZ7M49zdKfgnu/g0c2w/V/g+SxCkcizaRRPUj9+vXj+eefZ/To0axdu5ZBgwaRmZnJggUL6NmzJ//4xz8afKyPPvqI22+/nTfffJO0tDSmTJnCxx9/zKZNm4iOPvUHf+HChRw6dIiePXtit9uZNWsWjzzyCLNnz2bUqFEUFxdz4403MnHiRJKTkzl06BAPPvggNTU1/PDDD67jJCYmcvfddzNx4kTXYyEhIQQFNWzdD/UgNd72wlIuf+EbbFYLyx9PJyLIbnZJ0tas/QQ+vdt5//q3of9NLV9D8T5n79DW2jWJDrs/H9n9xDyixGFak0ikiTT093ejAlJwcDDr1q0jMTGRp556inXr1vHJJ5+wcuVKRo8eTX5+foOPlZaWxqBBg3jttdcAcDgcJCQk8MADD/DYY4816BgDBgxgzJgxPPPMM/U+v3z5cgYPHsyuXbvo1KkT4AxIDz30EA899FCDaz2ZAtL5ufrV71i3r4Rnr+vL+DQtLCommPcULHrJ2Zs04UvnJe/NqbIcdn1/YnJ14Ub35/3CoOulJ0JReKfmrUekjWrWITa73U55uXPjwnnz5nHFFVcAEBERQUlJSYOPU1lZyYoVK0hPP3HJrdVqJT09nZycnLO+3jAMsrOz2bRpE5dccslp2xUXF2OxWAgPD3d7/LnnniMyMpILL7yQP//5z1RXV5/2GBUVFZSUlLjdpPEyjl/BNlPDbGKWy/8Xuo+C6mPO7UiOFJz9NefCMCB/HSx+Bf75c3g+ET64AZa87gxHFit0HASXPgZ3z4XfbYex02DgnQpHIh6gUVexDRs2jMzMTIYOHcqyZcv46KOPANi8eTMdOzZ80m1RURE1NTXExLhvOxETE8PGjRtP8ypn4OnQoQMVFRXYbDZef/11Ro4cWW/bY8eO8eijjzJu3Di3pPjrX/+aAQMGEBERwffff8/kyZPJy8vjxRdfrPc4WVlZ/PGPf2zwucmZjekfR9aXG1m64yAFJceICW3jW0BIy7Pa4Ia34O1059VtH90Kd846vyvAyoqcG71um++8ldbpTQ/teHxNohHO3qKAdud3DiLSbBoVkF577TXuu+8+PvnkE9544w06dOgAwJdffsmVV17ZpAXWJyQkhFWrVlFaWkp2djaZmZl07dr1lFW8q6qquPnmmzEMgzfeeMPtudrFLQH69++P3W7n3nvvJSsrCz+/U/8HOXnyZLfXlJSUkJCgZfkbq2O7QAZ2bseKXYeYvSaPu4Z1MbskaYv8w5xXsr11GexdBrMz4ZrXGr5uUHWl83VbT16T6KRZC76BzvlDtVecRXXXmkQiXqJRAalTp07MmjXrlMdfeumlczpOVFQUNpuNggL3ru2CggJiY2NP+zqr1era6iQlJYUNGzaQlZXlFpBqw9GuXbuYP3/+WecJpaWlUV1dzc6dO+nRo8cpz/v5+dUbnKTxMvrHsWLXIWauyVVAEvNEdoMb/wEf3Ag/vg8x/eCiX52+fe2aRFuzYed3UFnq/nxMvxMrV3caojWJRLxUozfDqqmp4bPPPmPDhg0A9OnTh2uuuQabreFritjtdgYOHEh2djbXXnst4JyknZ2dzaRJkxp8HIfDQUVFhevr2nC0ZcsWFixY4FrI8kxWrVqF1Wqt98o5aR6j+8fx9Kyf+HH3YfYcLCchItDskqStShoBI5+Brx+Hr34P0T2dW5SAc02iHd+dmFx9aKf7awOjnGEoaQR0vQxCYuoeXUS8UKMC0tatWxk9ejT79u1z9bZkZWWRkJDA7Nmz6datW4OPlZmZyR133EFqaiqDBw9mypQplJWVMWHCBABuv/12OnToQFZWlut9UlNT6datGxUVFXzxxRdMmzbNNYRWVVXFjTfeyMqVK5k1axY1NTWuq+oiIiKw2+3k5OSwdOlSLrvsMkJCQsjJyeHhhx/m1ltvpV07zQloKdEh/lzUNZLvtx1g1po8/md4w//diDS5Ifc7tyNZ/SH8+w7n9iQ7voU9y8CoOdHO6gudLjoRimL6gbVR17uIiAdrVED69a9/Tbdu3ViyZAkREREAHDhwgFtvvZVf//rXzJ49u8HHGjt2LIWFhTzxxBPk5+eTkpLCnDlzXBO3d+/ejfWk//mUlZVx3333sXfvXgICAujZsyfvv/8+Y8eOBWDfvn18/vnngHP47WQLFixg+PDh+Pn5MX36dJ566ikqKiro0qULDz/8sNscI2kZGcnxfL/tADNX5yogibksFrh6ChRtgX0/wDfPn3guMunEPKLEYeAXbFqZItIyGrUOUlBQEEuWLKFfv35uj69evZqhQ4dSWlp6mle2HloHqWkcKqtk0LPzqHYYzMu8lKRo/eIRk5XkwX/vB9+A42sSjYB2WqtLpLVo6O/vRvUg+fn5ceTIkVMeLy0txW7XqsjScO2C7PysexQLNhUya00uD6VfYHZJ0taFxsFtM8yuQkRM1qiB86uvvpp77rmHpUuXYhgGhmGwZMkSfvWrX3HNNdc0dY3SytXuzTZzdS7aO1lERDxBowLSK6+8Qrdu3RgyZAj+/v74+/tz8cUXk5SUxJQpU5q4RGntRvaOwc/HyrbCMjbkndozKSIi0tIaNcQWHh7Of//7X7Zu3eq6zL9Xr16utYlEzkWIvy+X94zmy3X5zFyTS+94zekSERFzNTggne0KrwULFrjun267DpHTyUiOdwak1bn8blQPLFptWERETNTggPTjjz82qJ1+sUljXNYjmiC7jb2HjrJqz2Eu7KT1qERExDwNDkgn9xCJNLUAu42RvWP4bFUuM1fnKSCJiIiptPyreIzaq9lmrcmlxqGr2URExDwKSOIxfta9PaH+Puw/UsHynQfNLkdERNowBSTxGHYfK1f1jQOcayKJiIiYRQFJPErtMNuX6/KpqnGYXI2IiLRVCkjiUS7qGkFUsJ2DZZUs3lpkdjkiItJGKSCJR/GxWRndr3aYLc/kakREpK1SQBKPUzvM9vX6fI5V1ZhcjYiItEUKSOJxBnZqR1yYP0cqqvlmc6HZ5YiISBukgCQex2q1cHV/Xc0mIiLmUUASj1Q7zJa9YT/lldUmVyMiIm2NApJ4pH4dwugcGcjRqhrmbdhvdjkiItLGKCCJR7JYLGT0d/YiaZhNRERamgKSeKzaYbZvNhVSfLTK5GpERKQtUUASj9UjNoQLYoKprHHw9fp8s8sREZE2RAFJPJprmG2NFo0UEZGWo4AkHu3q48Nsi7cWcaC0wuRqRESkrVBAEo/WJSqIfh3CqHEYfLlOw2wiItIyFJDE412TrKvZRESkZSkgiccbc3xV7WU7D5JffMzkakREpC1QQBKPFx8ewKDEdhgGzF6rydoiItL8FJDEK2RomE1ERFqQApJ4hav6xmG1wKo9h9lzsNzsckREpJVTQBKv0D7Ej4u7RQEwc416kUREpHkpIInXyEh2TtaeuVrzkEREpHkpIInXGNUnFl+bhQ15JWzdf8TsckREpBVTQBKvER5o55Lu7QH1IomISPNSQBKv4rqabU0uhmGYXI2IiLRWCkjiVdJ7x+DnY2V7YRnrc0vMLkdERFopBSTxKsF+PozoFQ3oajYREWk+CkjidTL6O4fZZq3O0zCbiIg0CwUk8TqX9YwmyG5j3+GjrNx92OxyRESkFVJAEq/j72vjij6xgLYeERGR5uERAWnq1KkkJibi7+9PWloay5YtO23bGTNmkJqaSnh4OEFBQaSkpDBt2jTX81VVVTz66KP069ePoKAg4uPjuf3228nNdf9FevDgQcaPH09oaCjh4eHcfffdlJaWNts5StOqXTRy9to8ahwaZhMRkaZlekD66KOPyMzM5Mknn2TlypUkJyczatQo9u/fX2/7iIgIHn/8cXJyclizZg0TJkxgwoQJfPXVVwCUl5ezcuVK/vd//5eVK1cyY8YMNm3axDXXXON2nPHjx7N+/Xrmzp3LrFmz+Pbbb7nnnnua/XylaQxLak9YgC+FRypYuuOA2eWIiEgrYzFMnuWalpbGoEGDeO211wBwOBwkJCTwwAMP8NhjjzXoGAMGDGDMmDE888wz9T6/fPlyBg8ezK5du+jUqRMbNmygd+/eLF++nNTUVADmzJnD6NGj2bt3L/Hx8Wd9z5KSEsLCwiguLiY0NLSBZytN6bFP1zB9+R7GDe5E1vX9zC5HRES8QEN/f5vag1RZWcmKFStIT093PWa1WklPTycnJ+esrzcMg+zsbDZt2sQll1xy2nbFxcVYLBbCw8MByMnJITw83BWOANLT07FarSxdurTeY1RUVFBSUuJ2E3PVLhr55bo8qmocJlcjIiKtiakBqaioiJqaGmJiYtwej4mJIT8//7SvKy4uJjg4GLvdzpgxY3j11VcZOXJkvW2PHTvGo48+yrhx41xJMT8/n+joaLd2Pj4+REREnPZ9s7KyCAsLc90SEhLO5VSlGVzUNZKoYD8Ol1exaGuR2eWIiEgrYvocpMYICQlh1apVLF++nGeffZbMzEwWLlx4SruqqipuvvlmDMPgjTfeOK/3nDx5MsXFxa7bnj17zut4cv5sVgtj+ulqNhERaXo+Zr55VFQUNpuNgoICt8cLCgqIjY097eusVitJSUkApKSksGHDBrKyshg+fLirTW042rVrF/Pnz3cbZ4yNjT1lEnh1dTUHDx487fv6+fnh5+d3rqcozSwjOZ73cnbx9foCjlXV4O9rM7skERFpBUztQbLb7QwcOJDs7GzXYw6Hg+zsbIYMGdLg4zgcDioqKlxf14ajLVu2MG/ePCIjI93aDxkyhMOHD7NixQrXY/Pnz8fhcJCWlnYeZyQtbUCndnQID6C0opqFmwrNLkdERFoJ04fYMjMzeeutt3jvvffYsGED//M//0NZWRkTJkwA4Pbbb2fy5Mmu9llZWcydO5ft27ezYcMGXnjhBaZNm8att94KOMPRjTfeyA8//MAHH3xATU0N+fn55OfnU1lZCUCvXr248sormThxIsuWLWPx4sVMmjSJW265pUFXsInnsFotXN3fuSaS9mYTEZGmYuoQG8DYsWMpLCzkiSeeID8/n5SUFObMmeOauL17926s1hM5rqysjPvuu4+9e/cSEBBAz549ef/99xk7diwA+/bt4/PPPwecw28nW7BggWsY7oMPPmDSpEmMGDECq9XKDTfcwCuvvNL8JyxNLiM5nr9+u53sDQWUVVQT5Gf6P2sREfFypq+D5K20DpLnMAyDy1/4hh1FZbx8Swo/T+lgdkkiIuKhvGIdJJGmYLFYyKgdZludZ3I1IiLSGiggSatQu2jkN5v3U1xeZXI1IiLi7RSQpFXoHhNCz9gQqmoMvvrp9IuMioiINIQCkrQatb1IWjRSRETOlwKStBq1l/t/v+0ARaUVZ2ktIiJyegpI0mp0jgwiuWMYNQ6DL9dpmE1ERBpPAUlaFdcw2yoNs4mISOMpIEmrMub4MNuynQfJKz5qcjUiIuKtFJCkVYkLC2BwYgQAs9doTSQREWkcBSRpdTKSaxeN1DCbiIg0jgKStDpX9YvDaoHVe4vZdaDM7HJERMQLKSBJqxMV7MfQpCgAZmmYTUREGkEBSVqljP5aNFJERBpPAUlapVF9YvG1WdiYf4TNBUfMLkdERLyMApK0SmGBvlx6QXsAZqkXSUREzpECkrRarkUj1+RhGIbJ1YiIiDdRQJJWK71XDP6+VnYUlbE+t8TsckRExIsoIEmrFeTnw4heMYAma4uIyLlRQJJWrfZqtllr8nA4NMwmIiINo4AkrdrwHu0J9vNh3+Gj/LjnkNnliIiIl1BAklbN39fGFX1qh9m0aKSIiDSMApK0erVXs81ak0eNhtlERKQBFJCk1RuWFEV4oC9FpRUs3X7A7HJERMQLKCBJq+drs3JV3zgAZq7R1WwiInJ2CkjSJmQkOwPSl+vyqax2mFyNiIh4OgUkaRPSukTSPsSPw+VVLN5aZHY5IiLi4RSQpE2wWS2M6Xd8mE2LRoqIyFkoIEmbUXs129c/FXCsqsbkakRExJMpIEmbMaBTOB3CAyitqGbBxv1mlyMiIh5MAUnaDIvFwtXJuppNRETOTgFJ2pTavdmyN+yntKLa5GpERMRTKSBJm9InPpSuUUFUVDuY91OB2eWIiIiHUkCSNsU5zObsRdLVbCIicjoKSNLmZPR3zkP6dkshh8srTa5GREQ8kQKStDndY0LoGRtCVY3BV+vzzS5HREQ8kAKStEkZrmG2PJMrERERT6SAJG1S7dVs328rovBIhcnViIiIp1FAkjapU2QgyQnhOAz4cp16kURExJ0CkrRZtZO1dTWbiIjUZXpAmjp1KomJifj7+5OWlsayZctO23bGjBmkpqYSHh5OUFAQKSkpTJs27ZQ2V1xxBZGRkVgsFlatWnXKcYYPH47FYnG7/epXv2rqUxMPd3X/eCwWWL7zELmHj5pdjoiIeBBTA9JHH31EZmYmTz75JCtXriQ5OZlRo0axf3/9+2RFRETw+OOPk5OTw5o1a5gwYQITJkzgq6++crUpKytj2LBhPP/882d874kTJ5KXl+e6/b//9/+a9NzE88WG+TM4MQKA2Ws0zCYiIif4mPnmL774IhMnTmTChAkAvPnmm8yePZt33nmHxx577JT2w4cPd/v6wQcf5L333mPRokWMGjUKgNtuuw2AnTt3nvG9AwMDiY2NPf+TEK+WkRzP0h0Hmbkml4mXdDW7HBER8RCm9SBVVlayYsUK0tPTTxRjtZKenk5OTs5ZX28YBtnZ2WzatIlLLrnknN//gw8+ICoqir59+zJ58mTKy8vP2L6iooKSkhK3m3i/q/rGYrNaWLO3mJ1FZWaXIyIiHsK0gFRUVERNTQ0xMTFuj8fExJCff/rF+4qLiwkODsZutzNmzBheffVVRo4ceU7v/Ytf/IL333+fBQsWMHnyZKZNm8att956xtdkZWURFhbmuiUkJJzTe4pnigz2Y2hSFACz1miytoiIOJk6xNYYISEhrFq1itLSUrKzs8nMzKRr166nDL+dyT333OO6369fP+Li4hgxYgTbtm2jW7du9b5m8uTJZGZmur4uKSlRSGolMvrH8e3mQmauzmPS5d3NLkdERDyAaQEpKioKm81GQYH7juoFBQVnnBtktVpJSkoCICUlhQ0bNpCVlXVOAamutLQ0ALZu3XragOTn54efn1+j30M81xV9Ynn8P+vYVHCETflH6BEbYnZJIiJiMtOG2Ox2OwMHDiQ7O9v1mMPhIDs7myFDhjT4OA6Hg4qK81sJuXYpgLi4uPM6jninsABfLu3RHtAwm4iIOJk6xJaZmckdd9xBamoqgwcPZsqUKZSVlbmuarv99tvp0KEDWVlZgHMeUGpqKt26daOiooIvvviCadOm8cYbb7iOefDgQXbv3k1urvMX3aZNmwCIjY0lNjaWbdu28a9//YvRo0cTGRnJmjVrePjhh7nkkkvo379/C38HxFNkJMcz96cCZq7OJXPkBVgsFrNLEhERE5kakMaOHUthYSFPPPEE+fn5pKSkMGfOHNfE7d27d2O1nujkKisr47777mPv3r0EBATQs2dP3n//fcaOHetq8/nnn7sCFsAtt9wCwJNPPslTTz2F3W5n3rx5rjCWkJDADTfcwB/+8IcWOmvxROm9ognwtbHzQDnr9pXQr2OY2SWJiIiJLIZhGGYX4Y1KSkoICwujuLiY0NBQs8uRJjDpXyuZtSaPey7pyu9H9zK7HBERaQYN/f1t+lYjIp4iIzkecO7N5nDo7wYRkbZMAUnkuEsvaE+Inw95xcdYsfuQ2eWIiIiJFJBEjvP3tXFFH+cSEzNX62o2EZG2TAFJ5CQZyc6lHr5Ym0d1jcPkakRExCwKSCInGZoURbtAX4pKK1my/aDZ5YiIiEkUkERO4muzclU/Zy+ShtlERNouBSSROjL6O69m+3JdHpXVGmYTEWmLFJBE6hjcJYLoED9KjlXz3ZZCs8sRERETKCCJ1GGzWhjTX8NsIiJtmQKSSD1qF42c+1MBRytrTK5GRERamgKSSD0uTAinY7sAyiprWLBpv9nliIhIC1NAEqmHxWJx23pERETaFgUkkdOovZpt/sb9HDlWZXI1IiLSkhSQRE6jV1wI3doHUVHtYN6GArPLERGRFqSAJHIa7sNseSZXIyIiLUkBSeQMrj4+zPbt5kIOl1eaXI2IiLQUBSSRM0iKDqZ3XCjVDoM56/LNLkdERFqIApLIWbiG2dboajYRkbZCAUnkLK4+vqp2zrYD7D9yzORqRESkJSggiZxFQkQgF3YKx2HAl2s1zCYi0hYoIIk0QO2aSFo0UkSkbVBAEmmAMf3jsFjgh12H2Hf4qNnliIhIM1NAEmmAmFB/0rpEADBLvUgiIq2eApJIA+lqNhGRtkMBSaSBruobh81qYd2+ErYXlppdjoiINCMFJJEGigiyMywpCoBZa7T1iIhIa6aAJHIOaofZPl+di2EYJlcjIiLNRQFJ5Bxc0ScGu83K1v2lbCo4YnY5IiKtTnWNgxW7DvFK9haOVdWYVoePae8s4oVC/X0Z3qM9X/9UwMzVufSMDTW7JBERr2YYBjuKyli0tYjvthSxZNsBjlRUAzCgUzuGdY8ypS4FJJFzlJEcfzwg5fGbK3pgsVjMLklExKscKK1g8bYDLNpSyOKtB05ZXy4swJehSZEE+dlMqlABSeScjegVTYCvjd0Hy1mzt5jkhHCzSxIR8WjHqmpYvvMgi7Y4e4l+yitxe95uszKws7O36Gfdo+gTH4bNau4fnwpIIuco0O7DyN4xfL46l5mrcxWQRETqcDgMfsor4bstRSzaWsjynYeorHa4tekZG8LPukcxrHt7BidGEGA3r7eoPgpIIo2QkRzP56tzmbUmj9+P7oXV5L90RETMtvdQubOHaGsR328t4lB5ldvzsaH+rh6ii7tF0T7Ez6RKG0YBSaQRLrkgihB/H/JLjvHDrkMMPr4NiYhIW1F8tIqcbQdYtLWQRVuK2Hmg3O35YD8fLuoawbAkZy9Rt/ZBXjVnUwFJpBH8fGxc2SeWj1fsZebqXAUkEWn1Kqsd/Lj7kOtqszV7D+M4aTk4m9VCSkI4w5KcvUTJCeH42rx3NSEFJJFGykiO5+MVe/libR5PZvTGx4v/RyAiUpdhGGwuKGXR1iIWbSlk6Y6DlFe6r0vUtX0QPzveQ5TWNYJQf1+Tqm16CkgijXRxt0giguwcKKskZ/sBfta9vdkliYicl/0lx44HoiIWbS1i/5EKt+cjg+wMTYpiWPcohiVFER8eYFKlzU8BSaSRfGxWRveL5f0lu5m5OlcBSUS8TllFNct2HHRdbba5wH0jbj8fK4O7RDivNktqT8/YkDZzUYoCksh5yOgfz/tLdjNnXT7PXNsXPx/PukxVRORk1TUO1uwrZvHxq81+3H2IqpoTE4ksFugbH+a82iwpigGd2+Hv2zb/v2b6pImpU6eSmJiIv78/aWlpLFu27LRtZ8yYQWpqKuHh4QQFBZGSksK0adNOaXPFFVcQGRmJxWJh1apVpxzn2LFj3H///URGRhIcHMwNN9xAQUFBU5+atAGDEiOICfWj5Fg1320uMrscERE3tdt4TMvZyb3TfuDCZ+Zy/evf88LczSzbcZCqGoOO7QIYNziBqb8YwMo/jGTmA8N49MqeXJwU1WbDEZjcg/TRRx+RmZnJm2++SVpaGlOmTGHUqFFs2rSJ6OjoU9pHRETw+OOP07NnT+x2O7NmzWLChAlER0czatQoAMrKyhg2bBg333wzEydOrPd9H374YWbPns3HH39MWFgYkyZN4vrrr2fx4sXNer7S+litFq7uH8/fF+1g5ppc0nvHmF2SiLRxB8sqWby1iMXHrzaru41HqL8PF3eLcq1J1DkyyKRKPZvFMAzj7M2aR1paGoMGDeK1114DwOFwkJCQwAMPPMBjjz3WoGMMGDCAMWPG8Mwzz7g9vnPnTrp06cKPP/5ISkqK6/Hi4mLat2/Pv/71L2688UYANm7cSK9evcjJyeGiiy5q0PuWlJQQFhZGcXExoaHasLQtW7XnMNdOXUyg3caKP4z0uNVgRaR1O1ZVww87nZffL9payPrcEk7+ze5rszi38Th+tVm/DuZv42Gmhv7+Nq0HqbKykhUrVjB58mTXY1arlfT0dHJycs76esMwmD9/Pps2beL5559v8PuuWLGCqqoq0tPTXY/17NmTTp06nTEgVVRUUFFxYjZ/SUlJve2k7UnuGEZCRAB7Dh5l/sb9jOkfZ3ZJItKK1W7jseh4L9GyHQepqGcbj9qrzdK6RBBo15Tjc2Xad6yoqIiamhpiYtyHJGJiYti4ceNpX1dcXEyHDh2oqKjAZrPx+uuvM3LkyAa/b35+Pna7nfDw8FPeNz8//7Svy8rK4o9//GOD30faDovFQkb/eF5fuI2Zq3MVkESkye07fJRFWwpZtPUA328t4kBZpdvzMaF+DD2+QOPQpCiiQ/xNqrT18LpIGRISwqpVqygtLSU7O5vMzEy6du3K8OHDm/V9J0+eTGZmpuvrkpISEhISmvU9xXtkJDsD0vxN+yk5VtWqFksTkZZXcsy5jcfi42sSbS8qc3s+yG7joq6RrlCUFB3sVdt4eAPTAlJUVBQ2m+2Uq8cKCgqIjY097eusVitJSUkApKSksGHDBrKyshockGJjY6msrOTw4cNuvUhne18/Pz/8/Dx7Yz0xT8/YEJKig9m6v5S56wu4YWBHs0sSES9SVePgx92Hj/cSFbF6bzE1J+3jYbNaSO4YxrDu7RmWFEVKQjh2H9MvRG/VTAtIdrudgQMHkp2dzbXXXgs4J2lnZ2czadKkBh/H4XC4zQ06m4EDB+Lr60t2djY33HADAJs2bWL37t0MGTLknM5BpFbtMNtL8zYzc02uApKInJFhGGzdX3p8gcYilm4/QFndbTyighh2fMhsSLdI9Uy3MFOH2DIzM7njjjtITU1l8ODBTJkyhbKyMiZMmADA7bffTocOHcjKygKc84BSU1Pp1q0bFRUVfPHFF0ybNo033njDdcyDBw+ye/ducnNzAWf4AWfPUWxsLGFhYdx9991kZmYSERFBaGgoDzzwAEOGDGnwFWwi9bk6OY6X5m1m0ZYiDpZVEhFkN7skEfEg+48cc116v3hrEQUl7n/cR9Ru45EUybDu7enQirfx8AamBqSxY8dSWFjIE088QX5+PikpKcyZM8c1cXv37t1YrSe6EMvKyrjvvvvYu3cvAQEB9OzZk/fff5+xY8e62nz++eeugAVwyy23APDkk0/y1FNPAfDSSy9htVq54YYbqKioYNSoUbz++ustcMbSmnVrH0yf+FDW55YwZ10+v0jrZHZJImKi8spqlu44yKLjgWhj/hG352u38XCGoih6x4W2mW08vIGp6yB5M62DJPV585ttPPflRvp3DOP+y5Lw87Hi52PDz9d64r6P9fjXx+/7WDW5UqQVqHEYrN1XzKIthXy3pYiV9Wzj0Sc+lGFJznlEqYltdxsPMzX097cCUiMpIEl99hws52f/b8E5v87uc2qAstus+PmeCFENCVpna+/v1t7meh/91SrSOLsOlDnnEW0p4vttRZQcq3Z7vkN4gOvS+6FJURp69wAev1CkSGuUEBHI70f3ZMHGQiprHFRU11BR5aCi+vj9agcVVQ6OVde4rXRbWe2gstrBEapPf/Bm5GuznFPQOtdgdnJ7ez2Pt+VVfcW7HCqr5PttB1i01Xm12Z6D7tt4hPj7cHG3SNfVZomRgeoh9lLqQWok9SDJ+TAMg2qHcTwwHQ9O1fUEqlPClXvbytr7VSe1Oe1rT9w/VlWDw4N+8n2slmYJZvYGHsfHpsulpX4V1TWs2HmI746vR7Qut/iUbTwu7NSOnyVFMbR7FP07hOnfk4dTD5KIB7NYLPjaLPjarAT7mfNjWF3jaHCgqi+4Vda+/kwBr77wdrx99UkJrdphUF1Zc8plzi3FVhvQTgpU/j42Auw2Ao/f/H1r7/sQYLcRcPzr2jYBvjYC7D4n3T/e3tfZ3tdmUU+CF3A4DDbmH2HRVuc8ouU7D3Ksyn0bjwtighmW1J6fdY9icJcIgkz6GZbmpU9VpI3ysTl7ToJMWv+0uuZ4yDqXYHZ8KPJ8glltm5Mnz9Y4DMorayivrAGqmuV8bVYLgScFp5MD14n7tjrhq57A5Xqtj1t7X/VaNFpe8VG3eURFpe7beLQP8eNnx/c1G5oURUyotvFoCxSQRMQUtQEt0KQ5qzUO43jYqn8I8liVg/LKao5W1XD0eHg6WlXjfKzSwdGqaudjrsedt2PH29Q+V9tTVuMwOFJRzZGK5pln5muzHA9Wp/ZwnRy46u/5OhG46g9rPq1qntiRY1Us2X7QebXZ1iK2F7pv4xFot5HWJcI1j+iCGG3j0RYpIIlIm2SzWpwBwN68l1lX1ThcYalu4DoRqGqD14mwdbSyhvKq2gB2ahirPV7tSGVVjUFVTfUpV1E1FbuP9ZTeLNew4vFQ5W+3ue47w5jVFdjOFMYCfG3NeiVlVY2D1XsOu1atXrXnsNs2HlYL9O8Yzs+6O9cjurBTO23jIQpIIiLNyddmJSzASlhA028TYRgGlTWOBgeuE/erXT1irtdW1XCssobyqmq3HrPaCcm1V1oebqYhSH9f6yk9YKedA1YnpDlfYyXA18fVvtphsHT7ARZtLWLJ9oOU1um5S4wMZFj3KIYltWdI10jCArWNh7hTQBIR8VIWS+3yDDbCA5v++IbhvNKyNnAdO2ko8ayBq85wo9trq068vtaxKgfHqhwcKm+eANYu0JeLk6KcV5slRZEQ0QzfMGlVFJBERKReFosFf19n701zLHDocBgcqz41cJ1rGDvxfLVraLKmxqB/QpjrajNt4yHnSgFJRERMYbVajs9H0q8i8TyahSYiIiJShwKSiIiISB0KSCIiIiJ1KCCJiIiI1KGAJCIiIlKHApKIiIhIHQpIIiIiInUoIImIiIjUoYAkIiIiUocCkoiIiEgdCkgiIiIidSggiYiIiNShgCQiIiJShwKSiIiISB0+ZhfgrQzDAKCkpMTkSkRERKShan9v1/4ePx0FpEY6cuQIAAkJCSZXIiIiIufqyJEjhIWFnfZ5i3G2CCX1cjgc5ObmEhISgsViabLjlpSUkJCQwJ49ewgNDW2y43qS1n6OOj/v19rPsbWfH7T+c9T5NZ5hGBw5coT4+His1tPPNFIPUiNZrVY6duzYbMcPDQ1tlf/oT9baz1Hn5/1a+zm29vOD1n+OOr/GOVPPUS1N0hYRERGpQwFJREREpA4FJA/j5+fHk08+iZ+fn9mlNJvWfo46P+/X2s+xtZ8ftP5z1Pk1P03SFhEREalDPUgiIiIidSggiYiIiNShgCQiIiJShwKSiIiISB0KSCaYOnUqiYmJ+Pv7k5aWxrJly87Y/uOPP6Znz574+/vTr18/vvjiixaqtPHO5RzfffddLBaL283f378Fqz033377LRkZGcTHx2OxWPjss8/O+pqFCxcyYMAA/Pz8SEpK4t133232OhvrXM9v4cKFp3x+FouF/Pz8lin4HGVlZTFo0CBCQkKIjo7m2muvZdOmTWd9nbf8HDbm/LztZ/CNN96gf//+rkUEhwwZwpdffnnG13jL5wfnfn7e9vnV9dxzz2GxWHjooYfO2K6lP0MFpBb20UcfkZmZyZNPPsnKlStJTk5m1KhR7N+/v97233//PePGjePuu+/mxx9/5Nprr+Xaa69l3bp1LVx5w53rOYJztdS8vDzXbdeuXS1Y8bkpKysjOTmZqVOnNqj9jh07GDNmDJdddhmrVq3ioYce4pe//CVfffVVM1faOOd6frU2bdrk9hlGR0c3U4Xn55tvvuH+++9nyZIlzJ07l6qqKq644grKyspO+xpv+jlszPmBd/0MduzYkeeee44VK1bwww8/cPnll/Pzn/+c9evX19vemz4/OPfzA+/6/E62fPly/vrXv9K/f/8ztjPlMzSkRQ0ePNi4//77XV/X1NQY8fHxRlZWVr3tb775ZmPMmDFuj6WlpRn33ntvs9Z5Ps71HP/xj38YYWFhLVRd0wKM//znP2ds87vf/c7o06eP22Njx441Ro0a1YyVNY2GnN+CBQsMwDh06FCL1NTU9u/fbwDGN998c9o23vhzWKsh5+fNP4O12rVrZ7z99tv1PufNn1+tM52ft35+R44cMbp3727MnTvXuPTSS40HH3zwtG3N+AzVg9SCKisrWbFiBenp6a7HrFYr6enp5OTk1PuanJwct/YAo0aNOm17szXmHAFKS0vp3LkzCQkJZ/1Lydt422fYWCkpKcTFxTFy5EgWL15sdjkNVlxcDEBERMRp23jzZ9iQ8wPv/Rmsqalh+vTplJWVMWTIkHrbePPn15DzA+/8/O6//37GjBlzymdTHzM+QwWkFlRUVERNTQ0xMTFuj8fExJx2vkZ+fv45tTdbY86xR48evPPOO/z3v//l/fffx+FwcPHFF7N3796WKLnZne4zLCkp4ejRoyZV1XTi4uJ48803+fTTT/n0009JSEhg+PDhrFy50uzSzsrhcPDQQw8xdOhQ+vbte9p23vZzWKuh5+eNP4Nr164lODgYPz8/fvWrX/Gf//yH3r1719vWGz+/czk/b/z8pk+fzsqVK8nKympQezM+Q59mO7JIAw0ZMsTtL6OLL76YXr168de//pVnnnnGxMqkIXr06EGPHj1cX1988cVs27aNl156iWnTpplY2dndf//9rFu3jkWLFpldSrNo6Pl5489gjx49WLVqFcXFxXzyySfccccdfPPNN6cNEd7mXM7P2z6/PXv28OCDDzJ37lyPnkyugNSCoqKisNlsFBQUuD1eUFBAbGxsva+JjY09p/Zma8w51uXr68uFF17I1q1bm6PEFne6zzA0NJSAgACTqmpegwcP9vjQMWnSJGbNmsW3335Lx44dz9jW234O4dzOry5v+Bm02+0kJSUBMHDgQJYvX87LL7/MX//611PaeuPndy7nV5enf34rVqxg//79DBgwwPVYTU0N3377La+99hoVFRXYbDa315jxGWqIrQXZ7XYGDhxIdna26zGHw0F2dvZpx5aHDBni1h5g7ty5ZxyLNlNjzrGumpoa1q5dS1xcXHOV2aK87TNsCqtWrfLYz88wDCZNmsR//vMf5s+fT5cuXc76Gm/6DBtzfnV548+gw+GgoqKi3ue86fM7nTOdX12e/vmNGDGCtWvXsmrVKtctNTWV8ePHs2rVqlPCEZj0GTbb9G+p1/Tp0w0/Pz/j3XffNX766SfjnnvuMcLDw438/HzDMAzjtttuMx577DFX+8WLFxs+Pj7GX/7yF2PDhg3Gk08+afj6+hpr16416xTO6lzP8Y9//KPx1VdfGdu2bTNWrFhh3HLLLYa/v7+xfv16s07hjI4cOWL8+OOPxo8//mgAxosvvmj8+OOPxq5duwzDMIzHHnvMuO2221ztt2/fbgQGBhq//e1vjQ0bNhhTp041bDabMWfOHLNO4YzO9fxeeukl47PPPjO2bNlirF271njwwQcNq9VqzJs3z6xTOKP/+Z//McLCwoyFCxcaeXl5rlt5ebmrjTf/HDbm/LztZ/Cxxx4zvvnmG2PHjh3GmjVrjMcee8ywWCzG119/bRiGd39+hnHu5+dtn1996l7F5gmfoQKSCV599VWjU6dOht1uNwYPHmwsWbLE9dyll15q3HHHHW7t//3vfxsXXHCBYbfbjT59+hizZ89u4YrP3bmc40MPPeRqGxMTY4wePdpYuXKlCVU3TO1l7XVvted0xx13GJdeeukpr0lJSTHsdrvRtWtX4x//+EeL191Q53p+zz//vNGtWzfD39/fiIiIMIYPH27Mnz/fnOIboL5zA9w+E2/+OWzM+Xnbz+Bdd91ldO7c2bDb7Ub79u2NESNGuMKDYXj352cY535+3vb51aduQPKEz9BiGIbRfP1TIiIiIt5Hc5BERERE6lBAEhEREalDAUlERESkDgUkERERkToUkERERETqUEASERERqUMBSURERKQOBSQRERGROhSQRESawMKFC7FYLBw+fNjsUkSkCSggiYiIiNShgCQiIiJShwKSiLQKDoeDrKwsunTpQkBAAMnJyXzyySfAieGv2bNn079/f/z9/bnoootYt26d2zE+/fRT+vTpg5+fH4mJibzwwgtuz1dUVPDoo4+SkJCAn58fSUlJ/P3vf3drs2LFClJTUwkMDOTiiy9m06ZNzXviItIsFJBEpFXIysrin//8J2+++Sbr16/n4Ycf5tZbb+Wbb75xtfntb3/LCy+8wPLly2nfvj0ZGRlUVVUBzmBz8803c8stt7B27Vqeeuop/vd//5d3333X9frbb7+dDz/8kFdeeYUNGzbw17/+leDgYLc6Hn/8cV544QV++OEHfHx8uOuuu1rk/EWkaVkMwzDMLkJE5HxUVFQQERHBvHnzGDJkiOvxX/7yl5SXl3PPPfdw2WWXMX36dMaOHQvAwYMH6dixI++++y4333wz48ePp7CwkK+//tr1+t/97nfMnj2b9evXs3nzZnr06MHcuXNJT08/pYaFCxdy2WWXMW/ePEaMGAHAF198wZgxYzh69Cj+/v7N/F0QkaakHiQR8Xpbt26lvLyckSNHEhwc7Lr985//ZNu2ba52J4eniIgIevTowYYNGwDYsGEDQ4cOdTvu0KFD2bJlCzU1NaxatQqbzcall156xlr69+/vuh8XFwfA/v37z/scRaRl+ZhdgIjI+SotLQVg9uzZdOjQwe05Pz8/t5DUWAEBAQ1q5+vr67pvsVgA5/woEfEu6kESEa/Xu3dv/Pz82L17N0lJSW63hIQEV7slS5a47h86dIjNmzfTq1cvAHr16sXixYvdjrt48WIuuOACbDYb/fr1w+FwuM1pEpHWSz1IIuL1QkJC+M1vfsPDDz+Mw+Fg2LBhFBcXs3jxYkJDQ+ncuTMATz/9NJGRkcTExPD4448TFRXFtddeC8AjjzzCoEGDeOaZZxg7diw5OTm89tprvP766wAkJiZyxx13cNddd/HKK6+QnJzMrl272L9/PzfffLNZpy4izUQBSURahWeeeYb27duTlZXF9u3bCQ8PZ8CAAfz+9793DXE999xzPPjgg2zZsoWUlBRmzpyJ3W4HYMCAAfz73//miSee4JlnniEuLo6nn36aO++80/Ueb7zxBr///e+57777OHDgAJ06deL3v/+9GacrIs1MV7GJSKtXe4XZoUOHCA8PN7scEfECmoMkIiIiUocCkoiIiEgdGmITERERqUM9SCIiIiJ1KCCJiIiI1KGAJCIiIlKHApKIiIhIHQpIIiIiInUoIImIiIjUoYAkIiIiUocCkoiIiEgd/x+ejtuZHhwtzgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr: float = 4e-2\n",
    "n_epoch: int = 3\n",
    "n_splits: int = 3\n",
    "batch_size: int = 512\n",
    "verbose: bool = True\n",
    "\n",
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "if dataset.empty:\n",
    "    raise ValueError('Training data is inadequate.')\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "n_pre_train_groups = len(dataset[dataset['i'] == 2]['group'].unique())\n",
    "if n_pre_train_groups < n_splits:\n",
    "    print(\"Not enough groups for pre-training. Splitting into {} folds.\".format(n_pre_train_groups))\n",
    "    n_splits = n_pre_train_groups\n",
    "\n",
    "w = []\n",
    "plots = []\n",
    "if n_splits > 1:\n",
    "    sgkf = StratifiedGroupKFold(n_splits=n_splits)\n",
    "    for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "        print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "        train_set = dataset.iloc[train_index].copy()\n",
    "        test_set = dataset.iloc[test_index].copy()\n",
    "        trainer = Trainer(train_set, test_set, init_w, n_epoch=n_epoch, lr=lr, batch_size=batch_size)\n",
    "        w.append(trainer.train(verbose=verbose))\n",
    "        plots.append(trainer.plot())\n",
    "else:\n",
    "    trainer = Trainer(dataset, dataset, init_w, n_epoch=n_epoch, lr=lr, batch_size=batch_size)\n",
    "    w.append(trainer.train(verbose=verbose))\n",
    "    plots.append(trainer.plot())\n",
    "\n",
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "w = avg_w.tolist()\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.4596, 1.9309, 3.7153, -2.66, -1.4637, 0.0503, 2.0, -0.7728, 1.5, 2.0454, -0.279, 0.2395, 1.0861, 0.906, 0.3435]\n",
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,2d,6d,10d,16d,27d,1.0m,1.0m,1.3m,2.1m,4.7m\n",
      "difficulty history: 0,9.0,8.8,8.5,8.3,8.0,7.8,7.6,7.4,7.2,7.1\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,7d,20d,1.4m,2.4m,2.9m,2.8m,2.7m,2.7m,3.9m,4.7m\n",
      "difficulty history: 0,6.4,6.2,6.1,6.0,5.9,5.8,5.7,5.6,5.5,5.4\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,13d,1.2m,1.1m,1.0m,1.1m,1.1m,1.0m,1.1m,1.1m,1.0m\n",
      "difficulty history: 0,3.7,3.7,3.7,3.7,3.7,3.7,3.7,3.7,3.7,3.7\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,27d,4.9m,7.4m,11.0m,1.0y,1.1y,1.3y,1.4y,1.5y,1.6y\n",
      "difficulty history: 0,1.1,1.2,1.3,1.4,1.6,1.7,1.8,1.9,2.0,2.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(w)\n",
    "\n",
    "        \n",
    "requestRetention = 0.9\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w: List[float], dataset) -> None:\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "        self.model.calc_r_matrix(RevlogDataset(dataset))\n",
    "\n",
    "    def predict(self, t_history: str, r_history: str):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            stabilities, difficulties = output_t[-1][0]\n",
    "            retentions = torch.tensor(requestRetention)\n",
    "            r_m, count = self.model.query_r_matrix(stabilities.unsqueeze(0), difficulties.unsqueeze(0), retentions.unsqueeze(0))\n",
    "            weight = self.model.w[13] * torch.pow(torch.tensor(count / 300), self.model.w[14])\n",
    "            r_w = (weight * r_m + (1 - weight) * retentions).clamp(0.0001, 0.9999)\n",
    "            stabilities = stabilities * torch.log(retentions) / torch.log(r_w)\n",
    "            return stabilities, difficulties\n",
    "\n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        with torch.no_grad():\n",
    "            outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "            stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "            retentions = torch.exp(np.log(0.9) * fast_dataset.t_train / stabilities)\n",
    "            r_m, count = self.model.query_r_matrix(stabilities, difficulties, retentions)\n",
    "            weight = self.model.w[13] * torch.pow(torch.tensor(count / 300), self.model.w[14])\n",
    "            r_w = (weight * r_m + (1 - weight) * retentions).clamp(0.0001, 0.9999)\n",
    "            stabilities = stabilities * torch.log(retentions) / torch.log(r_w)\n",
    "            return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(w, dataset)\n",
    "preview_text = \"1:again, 2:hard, 3:good, 4:easy\\n\"\n",
    "for first_rating in (1,2,3,4):\n",
    "    preview_text += f'\\nfirst rating: {first_rating}\\n'\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * states[0])), 1)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    preview_text += f\"rating history: {r_history}\\n\"\n",
    "    preview_text += \"interval history: \" + \",\".join([f\"{ivl}d\" if ivl < 30 else f\"{ivl / 30:.1f}m\" if ivl < 365 else f\"{ivl / 365:.1f}y\" for ivl in map(int, t_history.split(','))]) + \"\\n\"\n",
    "    preview_text += f\"difficulty history: {d_history}\\n\"\n",
    "print(preview_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(tensor([13.1021]), tensor(3.7153))\n",
      "(tensor([37.0147]), tensor(3.7153))\n",
      "(tensor([33.1128]), tensor(3.7153))\n",
      "(tensor([30.4410]), tensor(3.7153))\n",
      "(tensor([32.3140]), tensor(3.7153))\n",
      "(tensor([6.6162]), tensor(6.4955))\n",
      "(tensor([2.2768]), tensor(9.1358))\n",
      "(tensor([4.8538]), tensor(8.8631))\n",
      "(tensor([10.2385]), tensor(8.6042))\n",
      "(tensor([16.4169]), tensor(8.3583))\n",
      "(tensor([25.8456]), tensor(8.1247))\n",
      "(tensor([29.8897]), tensor(7.9029))\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,13,37,33,30,32,7,2,5,10,16,26,30\n",
      "difficulty history: 0,3.7,3.7,3.7,3.7,3.7,6.5,9.1,8.9,8.6,8.4,8.1,7.9\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * states[0])))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "preview_text = f\"rating history: {test_rating_sequence}\\n\"\n",
    "preview_text += f\"interval history: {t_history}\\n\"\n",
    "preview_text += f\"difficulty history: {d_history}\"\n",
    "print(preview_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss before: 0.3113, loss after: 0.3090, improvement: 0.0022\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w, dataset)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "loss_before = dataset['log_loss'].mean()\n",
    "\n",
    "my_collection = Collection(w, dataset)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "loss_after = dataset['log_loss'].mean()\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp\n",
    "print(f\"loss before: {loss_before:.4f}, loss after: {loss_after:.4f}, improvement: {loss_before - loss_after:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.8805\n",
      "RMSE: 0.0236\n",
      "[0.09379485 0.89464217]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Last rating: 1\n",
      "R-squared: 0.6170\n",
      "RMSE: 0.0381\n",
      "[0.26723069 0.69809967]\n",
      "\n",
      "Last rating: 2\n",
      "R-squared: -0.7976\n",
      "RMSE: 0.0781\n",
      "[-0.04546933  0.96990836]\n",
      "\n",
      "Last rating: 3\n",
      "R-squared: 0.8757\n",
      "RMSE: 0.0254\n",
      "[0.06311622 0.93513992]\n",
      "\n",
      "Last rating: 4\n",
      "R-squared: -0.3840\n",
      "RMSE: 0.0351\n",
      "[0.52285233 0.46303704]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts, squared=False)\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_regression = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_regression, label='Weighted Least Squares Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "plt.show()\n",
    "plt.figure(figsize=(16, 12))\n",
    "for last_rating in (\"1\",\"2\",\"3\",\"4\"):\n",
    "    plt.subplot(2, 2, int(last_rating))\n",
    "    print(f\"\\nLast rating: {last_rating}\")\n",
    "    plot_brier(dataset[dataset['r_history'].str.endswith(last_rating)]['p'], dataset[dataset['r_history'].str.endswith(last_rating)]['y'], bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "difficulty_calibration = pd.DataFrame(columns=['difficulty', 'predicted_retention', 'actual_retention'])\n",
    "difficulty_calibration = dataset[['difficulty', 'p', 'y']].copy()\n",
    "difficulty_calibration['bin'] = difficulty_calibration['difficulty'].map(round)\n",
    "difficulty_group = difficulty_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=difficulty_group.index, height=difficulty_group['y'],\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Difficulty\")\n",
    "lns.append(lns1)\n",
    "\n",
    "difficulty_group = difficulty_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(difficulty_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(difficulty_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e6069_row0_col0, #T_e6069_row0_col1, #T_e6069_row0_col2, #T_e6069_row0_col3, #T_e6069_row0_col4, #T_e6069_row0_col5, #T_e6069_row0_col6, #T_e6069_row0_col7, #T_e6069_row1_col0, #T_e6069_row1_col1, #T_e6069_row1_col2, #T_e6069_row1_col3, #T_e6069_row1_col4, #T_e6069_row1_col5, #T_e6069_row1_col6, #T_e6069_row1_col7, #T_e6069_row1_col8, #T_e6069_row2_col0, #T_e6069_row2_col1, #T_e6069_row2_col2, #T_e6069_row2_col3, #T_e6069_row2_col4, #T_e6069_row2_col6, #T_e6069_row2_col7, #T_e6069_row3_col0, #T_e6069_row3_col1, #T_e6069_row3_col2, #T_e6069_row3_col4, #T_e6069_row3_col6, #T_e6069_row4_col0, #T_e6069_row4_col1, #T_e6069_row4_col2, #T_e6069_row4_col3, #T_e6069_row4_col4, #T_e6069_row4_col6, #T_e6069_row4_col7, #T_e6069_row5_col1, #T_e6069_row5_col2, #T_e6069_row5_col6, #T_e6069_row6_col1, #T_e6069_row6_col2, #T_e6069_row6_col4, #T_e6069_row6_col6, #T_e6069_row7_col1, #T_e6069_row7_col2, #T_e6069_row7_col6, #T_e6069_row8_col1, #T_e6069_row8_col2, #T_e6069_row9_col1, #T_e6069_row9_col2, #T_e6069_row10_col1, #T_e6069_row11_col2, #T_e6069_row12_col1, #T_e6069_row12_col9, #T_e6069_row13_col2, #T_e6069_row13_col6, #T_e6069_row13_col9, #T_e6069_row14_col2, #T_e6069_row14_col5, #T_e6069_row14_col6, #T_e6069_row14_col9, #T_e6069_row15_col0, #T_e6069_row15_col2, #T_e6069_row15_col5, #T_e6069_row15_col6, #T_e6069_row15_col7, #T_e6069_row15_col8, #T_e6069_row15_col9, #T_e6069_row16_col0, #T_e6069_row16_col2, #T_e6069_row16_col4, #T_e6069_row16_col5, #T_e6069_row16_col6, #T_e6069_row16_col7, #T_e6069_row16_col8, #T_e6069_row16_col9, #T_e6069_row17_col0, #T_e6069_row17_col2, #T_e6069_row17_col3, #T_e6069_row17_col4, #T_e6069_row17_col5, #T_e6069_row17_col6, #T_e6069_row17_col7, #T_e6069_row17_col8, #T_e6069_row17_col9 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row0_col8 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row0_col9, #T_e6069_row5_col9, #T_e6069_row8_col7 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row1_col9 {\n",
       "  background-color: #ffd5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row2_col5, #T_e6069_row5_col3, #T_e6069_row10_col3, #T_e6069_row10_col4, #T_e6069_row11_col3 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row2_col8, #T_e6069_row6_col8, #T_e6069_row8_col5, #T_e6069_row9_col3, #T_e6069_row11_col0 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row2_col9, #T_e6069_row4_col9, #T_e6069_row5_col8, #T_e6069_row12_col3, #T_e6069_row14_col0 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row3_col3, #T_e6069_row5_col7, #T_e6069_row8_col3, #T_e6069_row8_col4, #T_e6069_row9_col0, #T_e6069_row12_col5, #T_e6069_row16_col3 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row3_col5, #T_e6069_row6_col5 {\n",
       "  background-color: #d1d1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row3_col7, #T_e6069_row11_col4, #T_e6069_row13_col1, #T_e6069_row15_col3 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row3_col8, #T_e6069_row8_col6 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row3_col9, #T_e6069_row11_col6 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row4_col5, #T_e6069_row14_col1 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row4_col8 {\n",
       "  background-color: #a9a9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row5_col0, #T_e6069_row7_col4, #T_e6069_row11_col1, #T_e6069_row12_col2 {\n",
       "  background-color: #a5a5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row5_col4, #T_e6069_row9_col4, #T_e6069_row14_col3 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row5_col5, #T_e6069_row12_col0 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row6_col0, #T_e6069_row7_col5, #T_e6069_row12_col4 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row6_col3 {\n",
       "  background-color: #c9c9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row6_col7 {\n",
       "  background-color: #9d9dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row6_col9, #T_e6069_row10_col6 {\n",
       "  background-color: #ffe5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row7_col0, #T_e6069_row8_col0, #T_e6069_row10_col2 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row7_col3 {\n",
       "  background-color: #bdbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row7_col7 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row7_col8 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row7_col9 {\n",
       "  background-color: #ff8d8d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row8_col8, #T_e6069_row9_col6 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row8_col9 {\n",
       "  background-color: #ff8989;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row9_col5 {\n",
       "  background-color: #ff8181;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row9_col7 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row9_col8, #T_e6069_row12_col7 {\n",
       "  background-color: #ffa1a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row9_col9 {\n",
       "  background-color: #f20000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row10_col0, #T_e6069_row13_col4 {\n",
       "  background-color: #d5d5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row10_col5 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row10_col7, #T_e6069_row13_col7, #T_e6069_row15_col4 {\n",
       "  background-color: #ffadad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row10_col8 {\n",
       "  background-color: #ff6161;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row10_col9 {\n",
       "  background-color: #ea0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row11_col5 {\n",
       "  background-color: #ff9191;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row11_col7 {\n",
       "  background-color: #ffa9a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row11_col8 {\n",
       "  background-color: #ff6565;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row11_col9 {\n",
       "  background-color: #f00000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row12_col6 {\n",
       "  background-color: #ff7575;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row12_col8 {\n",
       "  background-color: #ee0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row13_col0 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row13_col3, #T_e6069_row17_col1 {\n",
       "  background-color: #b5b5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row13_col5 {\n",
       "  background-color: #ffb1b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row13_col8 {\n",
       "  background-color: #ff9595;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row14_col4 {\n",
       "  background-color: #ff6d6d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row14_col7 {\n",
       "  background-color: #fc0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row14_col8 {\n",
       "  background-color: #fe0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e6069_row15_col1 {\n",
       "  background-color: #c1c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e6069_row16_col1 {\n",
       "  background-color: #8585ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e6069\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_e6069_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_e6069_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_e6069_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_e6069_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_e6069_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "      <th id=\"T_e6069_level0_col5\" class=\"col_heading level0 col5\" >6</th>\n",
       "      <th id=\"T_e6069_level0_col6\" class=\"col_heading level0 col6\" >7</th>\n",
       "      <th id=\"T_e6069_level0_col7\" class=\"col_heading level0 col7\" >8</th>\n",
       "      <th id=\"T_e6069_level0_col8\" class=\"col_heading level0 col8\" >9</th>\n",
       "      <th id=\"T_e6069_level0_col9\" class=\"col_heading level0 col9\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row0\" class=\"row_heading level0 row0\" >0.710000</th>\n",
       "      <td id=\"T_e6069_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_e6069_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_e6069_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_e6069_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_e6069_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_e6069_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_e6069_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_e6069_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_e6069_row0_col8\" class=\"data row0 col8\" >0.67%</td>\n",
       "      <td id=\"T_e6069_row0_col9\" class=\"data row0 col9\" >1.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row1\" class=\"row_heading level0 row1\" >1.000000</th>\n",
       "      <td id=\"T_e6069_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_e6069_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_e6069_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_e6069_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_e6069_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_e6069_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_e6069_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_e6069_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_e6069_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_e6069_row1_col9\" class=\"data row1 col9\" >1.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row2\" class=\"row_heading level0 row2\" >1.400000</th>\n",
       "      <td id=\"T_e6069_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_e6069_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_e6069_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_e6069_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_e6069_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_e6069_row2_col5\" class=\"data row2 col5\" >-0.08%</td>\n",
       "      <td id=\"T_e6069_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_e6069_row2_col7\" class=\"data row2 col7\" ></td>\n",
       "      <td id=\"T_e6069_row2_col8\" class=\"data row2 col8\" >-0.96%</td>\n",
       "      <td id=\"T_e6069_row2_col9\" class=\"data row2 col9\" >-0.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row3\" class=\"row_heading level0 row3\" >1.960000</th>\n",
       "      <td id=\"T_e6069_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_e6069_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_e6069_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_e6069_row3_col3\" class=\"data row3 col3\" >-0.39%</td>\n",
       "      <td id=\"T_e6069_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_e6069_row3_col5\" class=\"data row3 col5\" >-1.86%</td>\n",
       "      <td id=\"T_e6069_row3_col6\" class=\"data row3 col6\" ></td>\n",
       "      <td id=\"T_e6069_row3_col7\" class=\"data row3 col7\" >-2.76%</td>\n",
       "      <td id=\"T_e6069_row3_col8\" class=\"data row3 col8\" >-1.11%</td>\n",
       "      <td id=\"T_e6069_row3_col9\" class=\"data row3 col9\" >-0.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row4\" class=\"row_heading level0 row4\" >2.740000</th>\n",
       "      <td id=\"T_e6069_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_e6069_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_e6069_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_e6069_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_e6069_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_e6069_row4_col5\" class=\"data row4 col5\" >-3.06%</td>\n",
       "      <td id=\"T_e6069_row4_col6\" class=\"data row4 col6\" ></td>\n",
       "      <td id=\"T_e6069_row4_col7\" class=\"data row4 col7\" ></td>\n",
       "      <td id=\"T_e6069_row4_col8\" class=\"data row4 col8\" >-3.43%</td>\n",
       "      <td id=\"T_e6069_row4_col9\" class=\"data row4 col9\" >-0.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row5\" class=\"row_heading level0 row5\" >3.840000</th>\n",
       "      <td id=\"T_e6069_row5_col0\" class=\"data row5 col0\" >-3.56%</td>\n",
       "      <td id=\"T_e6069_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_e6069_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_e6069_row5_col3\" class=\"data row5 col3\" >-0.09%</td>\n",
       "      <td id=\"T_e6069_row5_col4\" class=\"data row5 col4\" >-1.89%</td>\n",
       "      <td id=\"T_e6069_row5_col5\" class=\"data row5 col5\" >-1.38%</td>\n",
       "      <td id=\"T_e6069_row5_col6\" class=\"data row5 col6\" ></td>\n",
       "      <td id=\"T_e6069_row5_col7\" class=\"data row5 col7\" >-0.40%</td>\n",
       "      <td id=\"T_e6069_row5_col8\" class=\"data row5 col8\" >-0.65%</td>\n",
       "      <td id=\"T_e6069_row5_col9\" class=\"data row5 col9\" >1.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row6\" class=\"row_heading level0 row6\" >5.380000</th>\n",
       "      <td id=\"T_e6069_row6_col0\" class=\"data row6 col0\" >0.02%</td>\n",
       "      <td id=\"T_e6069_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_e6069_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_e6069_row6_col3\" class=\"data row6 col3\" >-2.08%</td>\n",
       "      <td id=\"T_e6069_row6_col4\" class=\"data row6 col4\" ></td>\n",
       "      <td id=\"T_e6069_row6_col5\" class=\"data row6 col5\" >-1.75%</td>\n",
       "      <td id=\"T_e6069_row6_col6\" class=\"data row6 col6\" ></td>\n",
       "      <td id=\"T_e6069_row6_col7\" class=\"data row6 col7\" >-3.87%</td>\n",
       "      <td id=\"T_e6069_row6_col8\" class=\"data row6 col8\" >-0.97%</td>\n",
       "      <td id=\"T_e6069_row6_col9\" class=\"data row6 col9\" >1.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row7\" class=\"row_heading level0 row7\" >7.530000</th>\n",
       "      <td id=\"T_e6069_row7_col0\" class=\"data row7 col0\" >-0.56%</td>\n",
       "      <td id=\"T_e6069_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_e6069_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_e6069_row7_col3\" class=\"data row7 col3\" >-2.53%</td>\n",
       "      <td id=\"T_e6069_row7_col4\" class=\"data row7 col4\" >-3.49%</td>\n",
       "      <td id=\"T_e6069_row7_col5\" class=\"data row7 col5\" >0.01%</td>\n",
       "      <td id=\"T_e6069_row7_col6\" class=\"data row7 col6\" ></td>\n",
       "      <td id=\"T_e6069_row7_col7\" class=\"data row7 col7\" >0.20%</td>\n",
       "      <td id=\"T_e6069_row7_col8\" class=\"data row7 col8\" >0.81%</td>\n",
       "      <td id=\"T_e6069_row7_col9\" class=\"data row7 col9\" >4.43%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row8\" class=\"row_heading level0 row8\" >10.540000</th>\n",
       "      <td id=\"T_e6069_row8_col0\" class=\"data row8 col0\" >-0.53%</td>\n",
       "      <td id=\"T_e6069_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_e6069_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_e6069_row8_col3\" class=\"data row8 col3\" >-0.43%</td>\n",
       "      <td id=\"T_e6069_row8_col4\" class=\"data row8 col4\" >-0.33%</td>\n",
       "      <td id=\"T_e6069_row8_col5\" class=\"data row8 col5\" >-1.09%</td>\n",
       "      <td id=\"T_e6069_row8_col6\" class=\"data row8 col6\" >-1.18%</td>\n",
       "      <td id=\"T_e6069_row8_col7\" class=\"data row8 col7\" >1.11%</td>\n",
       "      <td id=\"T_e6069_row8_col8\" class=\"data row8 col8\" >2.08%</td>\n",
       "      <td id=\"T_e6069_row8_col9\" class=\"data row8 col9\" >4.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row9\" class=\"row_heading level0 row9\" >14.760000</th>\n",
       "      <td id=\"T_e6069_row9_col0\" class=\"data row9 col0\" >-0.43%</td>\n",
       "      <td id=\"T_e6069_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_e6069_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "      <td id=\"T_e6069_row9_col3\" class=\"data row9 col3\" >-0.95%</td>\n",
       "      <td id=\"T_e6069_row9_col4\" class=\"data row9 col4\" >-1.96%</td>\n",
       "      <td id=\"T_e6069_row9_col5\" class=\"data row9 col5\" >4.86%</td>\n",
       "      <td id=\"T_e6069_row9_col6\" class=\"data row9 col6\" >2.10%</td>\n",
       "      <td id=\"T_e6069_row9_col7\" class=\"data row9 col7\" >-0.24%</td>\n",
       "      <td id=\"T_e6069_row9_col8\" class=\"data row9 col8\" >3.66%</td>\n",
       "      <td id=\"T_e6069_row9_col9\" class=\"data row9 col9\" >11.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row10\" class=\"row_heading level0 row10\" >20.660000</th>\n",
       "      <td id=\"T_e6069_row10_col0\" class=\"data row10 col0\" >-1.61%</td>\n",
       "      <td id=\"T_e6069_row10_col1\" class=\"data row10 col1\" ></td>\n",
       "      <td id=\"T_e6069_row10_col2\" class=\"data row10 col2\" >-0.49%</td>\n",
       "      <td id=\"T_e6069_row10_col3\" class=\"data row10 col3\" >-0.11%</td>\n",
       "      <td id=\"T_e6069_row10_col4\" class=\"data row10 col4\" >-0.02%</td>\n",
       "      <td id=\"T_e6069_row10_col5\" class=\"data row10 col5\" >1.87%</td>\n",
       "      <td id=\"T_e6069_row10_col6\" class=\"data row10 col6\" >0.99%</td>\n",
       "      <td id=\"T_e6069_row10_col7\" class=\"data row10 col7\" >3.22%</td>\n",
       "      <td id=\"T_e6069_row10_col8\" class=\"data row10 col8\" >6.14%</td>\n",
       "      <td id=\"T_e6069_row10_col9\" class=\"data row10 col9\" >11.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row11\" class=\"row_heading level0 row11\" >28.930000</th>\n",
       "      <td id=\"T_e6069_row11_col0\" class=\"data row11 col0\" >-0.98%</td>\n",
       "      <td id=\"T_e6069_row11_col1\" class=\"data row11 col1\" >-3.59%</td>\n",
       "      <td id=\"T_e6069_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "      <td id=\"T_e6069_row11_col3\" class=\"data row11 col3\" >-0.05%</td>\n",
       "      <td id=\"T_e6069_row11_col4\" class=\"data row11 col4\" >-2.67%</td>\n",
       "      <td id=\"T_e6069_row11_col5\" class=\"data row11 col5\" >4.24%</td>\n",
       "      <td id=\"T_e6069_row11_col6\" class=\"data row11 col6\" >-0.78%</td>\n",
       "      <td id=\"T_e6069_row11_col7\" class=\"data row11 col7\" >3.43%</td>\n",
       "      <td id=\"T_e6069_row11_col8\" class=\"data row11 col8\" >5.95%</td>\n",
       "      <td id=\"T_e6069_row11_col9\" class=\"data row11 col9\" >11.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row12\" class=\"row_heading level0 row12\" >40.500000</th>\n",
       "      <td id=\"T_e6069_row12_col0\" class=\"data row12 col0\" >-1.33%</td>\n",
       "      <td id=\"T_e6069_row12_col1\" class=\"data row12 col1\" ></td>\n",
       "      <td id=\"T_e6069_row12_col2\" class=\"data row12 col2\" >-3.56%</td>\n",
       "      <td id=\"T_e6069_row12_col3\" class=\"data row12 col3\" >-0.70%</td>\n",
       "      <td id=\"T_e6069_row12_col4\" class=\"data row12 col4\" >0.10%</td>\n",
       "      <td id=\"T_e6069_row12_col5\" class=\"data row12 col5\" >-0.38%</td>\n",
       "      <td id=\"T_e6069_row12_col6\" class=\"data row12 col6\" >5.33%</td>\n",
       "      <td id=\"T_e6069_row12_col7\" class=\"data row12 col7\" >3.63%</td>\n",
       "      <td id=\"T_e6069_row12_col8\" class=\"data row12 col8\" >11.35%</td>\n",
       "      <td id=\"T_e6069_row12_col9\" class=\"data row12 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row13\" class=\"row_heading level0 row13\" >56.690000</th>\n",
       "      <td id=\"T_e6069_row13_col0\" class=\"data row13 col0\" >-1.56%</td>\n",
       "      <td id=\"T_e6069_row13_col1\" class=\"data row13 col1\" >-2.75%</td>\n",
       "      <td id=\"T_e6069_row13_col2\" class=\"data row13 col2\" ></td>\n",
       "      <td id=\"T_e6069_row13_col3\" class=\"data row13 col3\" >-2.90%</td>\n",
       "      <td id=\"T_e6069_row13_col4\" class=\"data row13 col4\" >-1.57%</td>\n",
       "      <td id=\"T_e6069_row13_col5\" class=\"data row13 col5\" >3.09%</td>\n",
       "      <td id=\"T_e6069_row13_col6\" class=\"data row13 col6\" ></td>\n",
       "      <td id=\"T_e6069_row13_col7\" class=\"data row13 col7\" >3.15%</td>\n",
       "      <td id=\"T_e6069_row13_col8\" class=\"data row13 col8\" >4.20%</td>\n",
       "      <td id=\"T_e6069_row13_col9\" class=\"data row13 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row14\" class=\"row_heading level0 row14\" >79.370000</th>\n",
       "      <td id=\"T_e6069_row14_col0\" class=\"data row14 col0\" >-0.72%</td>\n",
       "      <td id=\"T_e6069_row14_col1\" class=\"data row14 col1\" >-3.09%</td>\n",
       "      <td id=\"T_e6069_row14_col2\" class=\"data row14 col2\" ></td>\n",
       "      <td id=\"T_e6069_row14_col3\" class=\"data row14 col3\" >-1.91%</td>\n",
       "      <td id=\"T_e6069_row14_col4\" class=\"data row14 col4\" >5.72%</td>\n",
       "      <td id=\"T_e6069_row14_col5\" class=\"data row14 col5\" ></td>\n",
       "      <td id=\"T_e6069_row14_col6\" class=\"data row14 col6\" ></td>\n",
       "      <td id=\"T_e6069_row14_col7\" class=\"data row14 col7\" >10.22%</td>\n",
       "      <td id=\"T_e6069_row14_col8\" class=\"data row14 col8\" >10.02%</td>\n",
       "      <td id=\"T_e6069_row14_col9\" class=\"data row14 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row15\" class=\"row_heading level0 row15\" >111.120000</th>\n",
       "      <td id=\"T_e6069_row15_col0\" class=\"data row15 col0\" ></td>\n",
       "      <td id=\"T_e6069_row15_col1\" class=\"data row15 col1\" >-2.42%</td>\n",
       "      <td id=\"T_e6069_row15_col2\" class=\"data row15 col2\" ></td>\n",
       "      <td id=\"T_e6069_row15_col3\" class=\"data row15 col3\" >-2.67%</td>\n",
       "      <td id=\"T_e6069_row15_col4\" class=\"data row15 col4\" >3.15%</td>\n",
       "      <td id=\"T_e6069_row15_col5\" class=\"data row15 col5\" ></td>\n",
       "      <td id=\"T_e6069_row15_col6\" class=\"data row15 col6\" ></td>\n",
       "      <td id=\"T_e6069_row15_col7\" class=\"data row15 col7\" ></td>\n",
       "      <td id=\"T_e6069_row15_col8\" class=\"data row15 col8\" ></td>\n",
       "      <td id=\"T_e6069_row15_col9\" class=\"data row15 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row16\" class=\"row_heading level0 row16\" >155.570000</th>\n",
       "      <td id=\"T_e6069_row16_col0\" class=\"data row16 col0\" ></td>\n",
       "      <td id=\"T_e6069_row16_col1\" class=\"data row16 col1\" >-4.79%</td>\n",
       "      <td id=\"T_e6069_row16_col2\" class=\"data row16 col2\" ></td>\n",
       "      <td id=\"T_e6069_row16_col3\" class=\"data row16 col3\" >-0.41%</td>\n",
       "      <td id=\"T_e6069_row16_col4\" class=\"data row16 col4\" ></td>\n",
       "      <td id=\"T_e6069_row16_col5\" class=\"data row16 col5\" ></td>\n",
       "      <td id=\"T_e6069_row16_col6\" class=\"data row16 col6\" ></td>\n",
       "      <td id=\"T_e6069_row16_col7\" class=\"data row16 col7\" ></td>\n",
       "      <td id=\"T_e6069_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "      <td id=\"T_e6069_row16_col9\" class=\"data row16 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6069_level0_row17\" class=\"row_heading level0 row17\" >217.800000</th>\n",
       "      <td id=\"T_e6069_row17_col0\" class=\"data row17 col0\" ></td>\n",
       "      <td id=\"T_e6069_row17_col1\" class=\"data row17 col1\" >-2.93%</td>\n",
       "      <td id=\"T_e6069_row17_col2\" class=\"data row17 col2\" ></td>\n",
       "      <td id=\"T_e6069_row17_col3\" class=\"data row17 col3\" ></td>\n",
       "      <td id=\"T_e6069_row17_col4\" class=\"data row17 col4\" ></td>\n",
       "      <td id=\"T_e6069_row17_col5\" class=\"data row17 col5\" ></td>\n",
       "      <td id=\"T_e6069_row17_col6\" class=\"data row17 col6\" ></td>\n",
       "      <td id=\"T_e6069_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "      <td id=\"T_e6069_row17_col8\" class=\"data row17 col8\" ></td>\n",
       "      <td id=\"T_e6069_row17_col9\" class=\"data row17 col9\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2c1567ca0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_Metric:  0.03886708198812505\n",
      "R-squared: -6.8749\n",
      "RMSE: 0.1527\n",
      "[0.68927589 0.25115013]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Metric of FSRS: 0.0150\n",
      "Universal Metric of SM2: 0.0747\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi4AAAIjCAYAAAAk1JABAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADBPElEQVR4nOydd3gc1bm439ld9V4tFzVXudvY2NhgXABDTEIJPeQmhCSkAAkJN7mk3Jtw88vlkgYBUm4gEEIwJE4ICWCMHWxjsIXlXmXZlotky+q97+7M74+RFslqK2l3Zz/pvM+zj1azs7PvnF3tfDrn+87RDMMwUCgUCoVCoRCAzWoBhUKhUCgUCm9RgYtCoVAoFAoxqMBFoVAoFAqFGFTgolAoFAqFQgwqcFEoFAqFQiEGFbgoFAqFQqEQgwpcFAqFQqFQiEEFLgqFQqFQKMSgAheFQqFQKBRiUIGLQqFQKBQKMajARaEYRRw6dIhbb72VzMxMwsPDGT9+PNdccw1PP/10t/2ysrLQNI2rr7661+M8++yzaJqGpmns3r3bs/3dd9/l3nvvZerUqURGRjJx4kS+8IUvcOHCBb+e13D44Q9/6DmXi2+//e1vPfudOXOGz33uc0yaNInw8HDS0tK48sor+cEPftDteCtWrOh2jIiICObMmcOTTz6Jrus9Xr+iooKvf/3r5OTkEBERQWpqKosWLeI//uM/aGxs9Pv5KxTS0NRaRQrF6GDHjh2sXLmSjIwMPvvZz5KWlkZxcTEffvghhYWFnDx50rNvVlYWZWVltLe3c/78edLS0roda8WKFezcuZPW1lZ27drFwoULAVi4cCHV1dXcdtttTJkyhVOnTvHMM88QGRnJ/v37exwnGPjhD3/Io48+ym9+8xuio6O7PbZ48WKmTJnCyZMnufTSS4mIiODee+8lKyuLCxcusHfvXt5++21aW1s9z1mxYgWFhYU89thjAFRWVrJ27Vp27drFd7/7XX784x979q2urmb+/PnU19dz7733kpOTQ1VVFQcPHuTNN9/k4MGDZGVlBaQdFAoxGAqFYlSwZs0aIyUlxaipqenxWFlZWbffMzMzjauuusqIjY01nnzyyW6PFRcXGzabzbjlllsMwNi1a5fnsffee89wu93d9n/vvfcMwPje977nu5PxIT/4wQ8MwKioqOhzn69+9auGw+Ewzpw50+Oxi9tu+fLlxsyZM7tta2lpMTIzM42YmBjD5XJ5tv/kJz8xAGP79u09jltXV2e0tLQM9nQUihGPGipSKEYJhYWFzJw5k/j4+B6Ppaam9tgWHh7OJz/5SdauXdtt+yuvvEJCQgLXXnttj+dceeWV2Gy2HtsSExPJz8/v1++BBx4gOjqa5ubmHo/dddddpKWl4Xa7Adi9ezfXXnstycnJREREkJ2dzb333tvv8YdDYWEhEyZMIDMzs8djvbXdxYSHh3PppZfS0NBAeXl5t+Pa7XYuu+yyHs+JjY0lPDx8eOIKxQhEBS4KxSghMzOTPXv2cPjwYa+f86lPfYq8vDwKCws929auXcutt95KSEiIV8dobGyksbGR5OTkfve74447aGpq4q233uq2vbm5mTfeeINbb70Vu91OeXk5q1ev5syZMzzyyCM8/fTT3H333Xz44Yden1dvVFdXU1lZ6bnV1NR4HsvMzKS4uJjNmzcP+fhnzpxB07RugWNmZiZut5uXXnppOOoKxejC6i4fhUIRGDZu3GjY7XbDbrcbS5YsMb797W8b77zzjtHe3t5j38zMTOP66683XC6XkZaWZvzoRz8yDMMwjh49agDGe++9Z7zwwgs9hop640c/+pEBGO+++26/++m6bowfP9645ZZbum3/y1/+YgDGtm3bDMMwjL///e9eva63dA4VXXzLzMz07HP48GEjIiLCAIx58+YZX//6143XX3/daGpq6nG85cuXGzk5OUZFRYVRUVFhHDt2zPjWt75lAMb111/fbd/S0lIjJSXFAIycnBzjy1/+srF27VqjtrbWJ+emUIxEVOCiUIwi8vLyjJtvvtmIjIz0XKBTUlKMf/zjH9326wxcDMMwvva1rxkzZswwDMMwvve97xnp6emGruteBS7vvfee4XA4jNtvv90rv4ceesiIiIgwGhoaPNtuueUWY/z48Yau64ZhGMaWLVsMwPjBD37Qa9A1WDoDl7/97W/Gpk2bPLcPPvig234FBQXGpz/9aSM+Pt7TdtHR0cbvfve7bvstX76810Dohhtu6DWPpqSkxPjyl79sjBkzxrNvaGio8d///d+ec1YoFB+hAheFYhTS1tZm5OXlGd/5zneM8PBwIyQkxDhy5Ijn8a6By4cffmgAxv79+43s7GzjW9/6lmEYxoCBS35+vpGYmGjMmzfPqK+v98orNzfXAIyXX37ZMAzDaGhoMCIiIoyHHnrIs4+u657E4NjYWOOGG24wnn/+eaO1tXVIbeFNcm5XXC6XcfDgQeN//ud/PEHMpk2bPI8vX77cyMrKMjZt2mS88847xq9//Wtj/PjxxpIlS7oFZBej67pRUFBgPPXUU8b48eMNwHj22WeHdE4KxUhGBS4KxSinMwD54Q9/6NnWNXAxDMOYNGmSsWLFCgMw9u3b1+15vQUuRUVFRnp6upGdnW2UlJQMyicrK8u48cYbDcMwjLVr1xqAkZub22O/3Nxc47vf/a6xYMECAzBmzpzZb2DQF4MNXLrS2ftzzz33eLb1VlV04MABw263Gw8++KBXxy0sLDRsNpuxYsWKQTspFCMdlZyrUIxyOudg6W+SuLvuuoutW7cyffp05s2b1+/xqqqqWL16NW1tbbzzzjuMHTt2UD633347GzZsoL6+nj//+c9kZWX1WnVz2WWX8eMf/5jdu3fz8ssvc+TIEV599dVBvdZw8abtAObMmcOnP/1p/u///o+ioqIBjztx4kQSEhKCeuI+hcIqVOCiUIwStmzZgtHLfJPr168HYNq0aX0+9wtf+AI/+MEP+PnPf97vazQ1NbFmzRrOnz/P+vXrmTJlyqA977jjDtra2njxxRfZsGEDt99+e7fHa2pqepxHZzDV1tbm2VZYWNitGmo4vP/++zidzh7bvWm7Tr797W/jdDr5xS9+4dm2c+dOmpqaeuybl5dHVVWVV8dVKEYbDqsFFApFYHjwwQdpbm7m5ptvJicnh/b2dnbs2OHp1fjc5z7X53MzMzP54Q9/OOBr3H333eTl5XHvvfeSn5/fbe6W6OhobrrppgGPcckllzB58mS+973v0dbWxh133NHt8RdffJFf//rX3HzzzUyaNImGhgaeffZZYmNjWbNmjWe/q666CjDLkIfL448/zp49e/jkJz/JnDlzANi7dy9//OMfSUxM5KGHHhrwGDNmzGDNmjU899xz/Od//idJSUm89NJLvPzyy9x8880sWLCA0NBQ8vPzef755wkPD+e73/3usN0VihGH1WNVCoUiMLz99tvGvffea+Tk5BjR0dFGaGioMXnyZOPBBx/sdebci0t3L6a3HJfMzMxeK2q4qLx4IL73ve8ZgDF58uQej+3du9e46667jIyMDCMsLMxITU01Pv7xjxu7d+/ucQ7evKY3OS7bt2837r//fmPWrFlGXFycERISYmRkZBj33HOPUVhY2G3f3nJcOtm6daunIsowDOPgwYPGt771LeOSSy4xEhMTDYfDYYwdO9a47bbbjL179w7orlCMRtRaRQqFQqFQKMSgclwUCoVCoVCIQQUuCoVCoVAoxKACF4VCoVAoFGJQgYtCoVAoFAoxqMBFoVAoFAqFGFTgolAoFAqFQgxqAroB0HWdkpISYmJi0DTNah2FQqFQKMRgGAYNDQ2MGzcOm803fSUqcBmAkpIS0tPTrdZQKBQKhUIsxcXFTJgwwSfHUoHLAMTExABw+vRpEhMTLbbxHqfTycaNG1m9ejUhISFW6wwKqe5SvUGuu1RvkOsu1Rvkukv1BqiuriY7O9tzLfUFKnAZgM7hoZiYGGJjYy228R6n00lkZCSxsbHiPuhS3aV6g1x3qd4g112qN8h1l+oNeBYn9WWqhZryfwDq6+uJi4ujtraWuLg4q3W8pnNcUWJujlR3qd4g112qN8h1l+oNct2legPU1dURHx9PXV2dz/75V1VFI5iIiAirFYaMVHep3iDXXao3yHWX6g1y3aV6+wMVuHiJy+WyWmFQuFwu1q9fL84b5LpL9Qa57lK9Qa67VG+Q6y7VG/xz7VSBi0KhUCgUCjGowEWhUCgUCoUYVOCiUCgUCoVCDKqqaAAkVxW5XC4cDoe4LHSp7lK9Qa67VG+Q6y7VG+S6S/UGVVWkGCQtLS1WKwwZqe5SvUGuu1RvkOsu1Rvkukv19gcqcPESadncLpeLLVu2iPMGue5SvUGuu1RvkOsu1Rvkukv1BlVVpFAoFAqFYpSjAheFQqFQKBRiUIHLCMbhkLsUlVR3qd4g112qN8h1l+oNct2levsDVVU0AJ1VRb7MiFYoFKMbl1untsWJBiREhmKzyaoUUSi8xR/XUBXCeYmu61YrDApd16msrCQ5ORmbTVbHmlR3qd4g112ad7tL58NTVXx4qoqKhlYSbK3ExieydEoK89PjRZS6Smvzrkh1l+oN/rl2ymoBC3G73VYrDAq3201ubq44b5DrLtUb5LpL8na6ddbtKebVvCJKaluIDbMz01HGibJ6/rD9DP/KL0NCB7ikNr8Yqe5SvcE/104VuCgUCkUAOHiujtyTVaTFhZOZFEVMuNnhnZ0cRVSonY1HyjhXo+bqUCgGQgUuCoVCEQB2n6nGwCAmPASXW+d8bStlHXFKSkwY9a1ODp6rtdRRoZCAynHxEgljz13RNI2YmBhx3iDXXao3yHWX4t3Q6iTvTDUXalvYW1RLWX0rugHgYEzMeaaPiyXMYaOsvs1q1QGR0ua9IdVdqjf459qpqooGQFUVKbzBrRucrmykttnJmNhwJiREiPySUfiGuhYnu89Us/N0NTtPVXG4pB633v2rNjLUTqvTTedmmwYzx8Xy8OppLJuSgl1VGilGAKqqyEIkVhUVFxeTnp4uMgtdkntLu5tX8s5ysLiWZK2RWlsMV0xJ4Ya548WUuUpr806CxbuqsY1dZ6r58FQ1eaeryS+t5+J/CZOiQglz2Jg+NpYJCRHEhTuwt9awu8LGkQv1VDe1c+h8Pfe8sIsxsWF88pIJ3LpgApNSoq05qT4IljYfClLdpXqDf66dKnDxEmnZ3G63m/379zNu3DhxH3Rp7h+eriLvdA3jYkNIaqqhOSSWzcfKmTImhpnjZKwoLq3NO7HKu7y+lQ9PV5N3uoqdp6o5Ud7YY5+JyVEsyk5k8cREFmUnEWq38ZutJ7lQ10pEqB2bZpBFOU1jJhIeYiM1NhxNgzcPXqCsvo3fbC3kN1sLuSQjntsWpnP9nLHEhocE7Bz7QupnBeS6S/UG/1w7VeCiUAyTg8W1FNc08/6JJpraHdi088SGh9Di1Lnz0nQWZCYQHxlqtaZiGJyraWZnR2/KztNVnKlq7rHP1DHRLM5OMgOVrERSY8N77POZJVn8ZXcxZ6ua0Qw32TFQ2djO/IwE7rg0naToMP7z4zN4N7+cv+45x9aCcvYW1bK3qJZH3zjCdTPTuG1hOksmJonpzVMofI0KXBSKIVLX4uSl3DP837ZTNLeb/1XYNQO3oVHb4mTT0TI2HS0DYNqYGC7NTuDSrEQuzUpkXHyEleqKfjAMgzNVzZ7elJ2nqzlf271MWdNgxthYFmcnsSg7kUXZiSRGDRycZiVH8fWrp1BQ2sCFmkZaT5Vw35UTmTI23pPTEuaws2b2WNbMHkt5fSuv7TvPut3FFFY08fr+El7fX8L4+AhuuWQ8tyyYQGZSlF/aQaEIVlTg4iXSEi01TSMlJUWcNwS/e1VjG7//4DQv5Z6loc1csj0qzM6U5EhuGN/CtppYnLpGUnQYh8/XcaqyiYKyBgrKGvjTh0UAjI+PYFF2Ykcgk8Dk1GhLzzfY27wvfOFtGAYnyxv5sCORNu90NeUN3at77DaN2ePjWNwx9LMgM5G4iKEN24Q57MyZEM+MtGjyGlKYPCamz0Tc1Nhwvrx8El+6ciL7i2v5655z/PNACedrW3hq80me2nySRdmJ3LZgAmtmjyUqzP9f6RI/K4Zh0NDmoqmljfjEJFra3YSEWD/s5i0S27wTVVVkAaqqSNFJaV0rv9t2irV5Z2l1mglnU8dE89UVkxifEMn7xyuoamonPTGSa2aM8SRVVja2sftMNXmna9h1ppojJXVcVGBCQmQIC7PMIYZLsxOZOS6WELussexA0+p0c7K8keZ2N3ERIUxKicLhRZu5dYNjpfWeoZ+8M9VUN7V32yfUbmNeerwnR+WSjISABAXe0Op0886RUv665xwfnKz0JAFHhpo9NbctmMCi7ESRFzlf0+p0k3+hnrzT1ZyubMLp1tE0jVC7xszxcSzITGBySrRXnxvF0PDHNVQFLgPQ2ejV1dUkJCRYreM1brebEydOMGXKFOx2u9U6gyLY3IuqmvnNe4X8bc852t1mwDJnQhz3r5zMNdPHeHINnE4XBcePkzNtar8ruTa2udhXVMOujovm/uJaTyDUSUSInfkZ8VyaZQ5DzM+IJzLUfxfOYGvzgdhbVMP6gxcorWshO6SOs644spKjufmSCWQndx86cbl1DpfUe4Z+dp2ppr7V1W2f8BAbl2QkeIZ+5mfEEx7i33bwRZuX1Lbw946hpK55NxmJkdy6YAKfvGQ8ExIifaUMyPmsHDxXyxsdvVMaGklRoYTaIMWopsgdT3WzkxC7RnZyNLcumEB6om/byZdIafPeqKmpITExUZVDW4HEcuiCggImTZok7oMeLO4nyhr49dZC/nmgxDMHx6LsRB5YOZllU5J7+Y/WoPDkCaZNndLvcaPDHCybksKyKSmAufDe4ZI6dp02L6q7ztRQ1+JkR2EVOwqrAHOoYta4WHNoKTuRhZkJJEWH+excg6XNvSH/Qj2v7Cyi3aWTmRDOZOc5nFGpnChv5I87zvD5K7IprW9l5+lqPjxVxd6zNTS1d69siAq1szDL7E1ZnJ3I7PHxhDoC+1+3L9p8XHwE96+czFdXTGLP2RrW7T7HmwdLKKpu5hebjvPEv46zdFISty1I59qZaUSEDu+9NQyD1nYnBQUFpGdmExURnJ+VD09V8dc952h3uclMjPK8t5rhJq2lhpboFJJjwmlud3G8rIHff3CaT1+WyeTU4Co970TS3+fFqHJohSIAHDpXx6+2nGTDkVLPtuVTU7h/5WQWZSf6/PVCHeZ/+5dkJPCl5ZPQdYOTFY3kdQYyp6spqWvlwLk6Dpyr47kPTgMwKSWqS55M4qiY9M4wDLYdr6CpzcXk1Ghs6LS3QkVrO1WNbXx4qoo/7TyL0929IzkuIoRLsxI9OSozxsaOqOEBTdNYmJXIwqxEfnDDDDYcLmXd7nPknqpi+0nzFhPm4ONzx3LrgnQuyRjcStSNbS4Onasj73QVlQ0tLLTB4xuOkZEcw6VZieSMjSHMERwX1KMl9fxtzzkMwyArKarf84wMdTA5NZrCiiZeyTvLF5dNIi2uZzWYIrhQgYtC0cGuM9U8s/kk7x2v8Gy7bmYa96+czOwJgZuPxWbTmDomhqljYvj0ZZkAnK9t8Qwt7T5TzfGyRgormjq+cIsBSIsN59LsRBZlJXBpdiJTU2NElsy6dYO6FifVTW1UN5k/q5raqWlqp6SulQ9OVOJ06+w6W0NLu4vmdju6caHbMZKiQj1lyYsnJjFtjMy2GAqRoQ4+eckEPnnJBIqrm/nb3nP8dc85ztW08EpeMa/kFTMxJcocSpo/od8LtWEYfHCykk1Hy6hoaMNh14gPt3c8BvuKatlXVMv4hAhumDuOWeOtnbfIrRtsPFpKc7uLSSneJbzbNI1JyVEUlDXwwckKbl2QHgBTxXBQOS4DIDnH5eDBg8yZM0dc12Ig3Q3D4P0TlTyz5SR5p6sBc1jmhrnj+OqKSUwZE+P1sQLpXdPUzu6zNR1DS9UcOleH66KM39hwBws7emMWZScwa3xcj/+KXW6d/AsNnCqrR6stImfGLKakxfq056bV6aamuZ2qxnaqm9q73a9ubqe66/2mdmqb23skLw9EVKid8QkRRIc5mJgSxU9vnRv0vU+B/LzousHO09Ws21PM24dKaXGaQ2c2DZZNSeHWBRO4ZsaYbnk9hmHw9qFS1h++QLjDzpjYMBx2G5qhk9ReSlVoGoZmo83l5nxNCxGhdu64NJ0Fmb7vlfSWE2UNPLP5JCkxYb0mU1/s3pWKhjbA4NvX5QTdvEuSv8/9keOiApcBUFVFIxNdN9iUX8avtpzk4Lk6AELsGrcuSOcryyeRkRS8iXq90dLuZl9xDbtO17D7bDV7ztZ45pbpJMxhY256vKdyae6EODYcLuWDk2YPhoGZf/OJOWNZmTOm19cxDIP6VpcZaHhuPXtGugYlF+eXeEtsuIOk6DASIkNIjAojKSqU+MgQjpbU09DmJCMxiohQO1GhdqI7LlIFZY1cPT2V2xaq/5r7orHNxfqDF1i3p5hdZ2o82+MiQrhh7jhuXTCBORPi+PBUNWvziojreB/6wzAMimtaCHXY+OKyiZbliryaV8TWggqmpXn/D0cnbt0si//U4gyunJriBzvv6SzfLq9vo7y+laZ2N27dwGHTiI0IYUxsGKkx4cPOWQoEqqrIAlSPS+Dxp7vLrfPWoQv8astJjpeZ07SHh9i4a1EG9105kbFxQ58YLpja3OXWOdpRBrr7jNkzU3VRya9Ng5hwB+PiI5iYFEkqdRQ0R9DmMliQlUir090tEOm8f3HPjjc4bBoJUaEkRYWSGBXquZ8QGUpStLktMTKUxI77CZGhfZaD552u5qXcM0SHOUiNDiHZWUa5YwzFta2Ehdj5yvJJZCUH/6RswfB5OVPZxF/3nONve89xoa7Vs31KanTH+xDCtLTuF5u+ei0Mw+BEWSOXTkzkc0uzLOnx+n9vHqW+1cnYuAjcukG7W6fd9dHN6XIx3lZLSMI4sPVs8xPlDVw+KZm7O4ZoA02by01BaQO7TldzsryRxjYXTt3AjsH0sBry2xJwGxphITaiwxzM7ijpnpgSHbSLcqqqIguRWFVUVFTErFmzLL+IDhZ/uLe7dF7be47fvFfI2Y6y0ZgwB/+2JJN7r8gm2QcVOsHU5g67jTkT4pkzIZ4vLDMvKqcqmzoql8xApqi6mboWF3UtDeRfaOh4pnnx2lNU2+/xo0LtvQYiiVFhJEaFdPwM9dxiwx0+u5AtzEygprmdd/PLOFnewMTYOnbWRJEYE8FNl4wXEbRAcHxespKj+Pdrp/GNa6ayo7CSdbvP8c6RUs/aSxpmL9aMsbFkJ0d1XBwNYt11VNG9V07TNFJiw8gvqae0vnVI/wQYhkGbS6eh1UVjm4vGVhcNbU4aO39vc3V77KPfnTS0uiiqbsbp0nHqRo/VuLsSGXqWzMRIMpOiyEiM9PRchNhsNLW5+nyevzAMg4Pn6nj78AWKq1swMEiKCmN8QgShdhs2dNJbzuOKm4COjVaXTmOri60FFeQWVjFlTAzXzxnbYyqAYEBVFSkUg6Sl3c2ru4r43bZTnv8oEyJD+PwV2fzbkqwhz34qDU3TmJQSzaSUaO5clAHA3/YW88cdZzGAC7Ut2Nxt2EPDcRsa8zPimZxi/tedFG32gHS97+85TvrDZtO4dmYacyfEc/R8NQ0nLnDHonTmpCcRFzk63k9fY7dpnhL9uhYn333tENtPVlLb4uR0ZROnK5uICLEzLS2GGWlRZF905TAMs3fDYdM4X9vCP/eXMGNcbEfg4eoj8HB2/71jv6H06PWHw6YR6rARarcRYteoa26jud1NfmkD+aVmwD4mNozMxChC7FrAey7qWpy8fegCuYVVGBikJ0b0rNDq0iSaphERYicixE5KTBhNbS6OlNRRVN3EVdPHsGJaStBUePkLFbgoRiQNrU5e+vAsv3//tGeIJDUmjPuunMinFmf4dTI3KSyZmMzu0zW0ON0sn5TA5PaTbGqIY1xiNN+8ZqqlwYk3pMWFkxSZwvoTcNnEJFFTuAczcREhpCdGcPWMMUSE2Dl6oZ78C/U0t7vZX1zL/uJaNoXbcduKaXcb5jCMu/t/1Z3zDw0VTYPoUAfR4Q6iwz76GdP5e1gI0eEOYsK677PhcCllda1kJ0d5gpWu1WSa4Sa96Tg72tI5U93K2eomKhvbKatvo6zeXOZhT1ENu8/WsHxqCldOTfHrumKVjW28lHuW/Av1jIuPGNI/UlFhDqakRlPR0Mbf952ntK6V2xemi8h/GSrq29tLpC0lbrPZmDZtmjhvGJ57TVM7L2w/zR92nPHMjjohIYIvL5/ErQsm+PViLK3Nx8VHcOvCdP554DynqlrQwmLJSIzitkUZQR+0dCKtzbsSzO4ut4FNg8SoUK6YnMzSiUmcrW7m6IV6Tlc0UtGqAc4ez7NpZu9NdJiDtLiIHoFFTNhH96PCugceZlBiBiSRIfYhla+H2m28lHuWqDBHrz0nBhoNoclMiIpkQlI0V5BMY5uLoqpmCisaKa5pptWp8/bhUt4+bM7jNCU1miunprB8agqLshN99rdR09TOH3PPUFDawOTU6H6X+DDQqHEkY9B7m2iaRmpsOFFhDnYUVmJgcOelwfF37I/Pt0rOHQBVVSSD8vpWnn3/FC/vLPJU00xKieKrKyZzw7xxat2ffmhsc3G2qgmHzUZWcuSI72ZWDMwTmwooqm4ho5dp8FucbsrqWnHYNULtNrNno6N3w27TOF7WyHWzxnDT/AkB965vdfKTDcdwuQ3GxA5uIrlTFY1MTIli+dRU3j9RyXvHy9lfXNutND/MYeOyiUmeQGZSSv8T3PWFy63zhx1nyDtdPWDQMlia2lycq2nm43PG8fG543x23KHij2uo6nHxEpcr8Albw8HlcpGXl8eiRYv6XTcnGBmMe3F1M/+3rZC/7D5Hu8vsrp45Lpb7V07muplpAZ10TGqbR4c5mJYaRV5eHpOSF1mtMyiktjkEt3tOWizHShswDKPHhTnSoXFZdBVlYRN6zIXS7tKxaZCeaE2SaGx4CIuzk3jzYAmxESFEXNTjoBk6Y9rO9XCvbTaHky+fnMIlmQlckpnA16+eQl2zkw9OmkHMtuOVlNa38t7xCt47XsGPMFd5N4OYZJZOTiY23Luhnp2nq9lXVEtGYqRXQUtf3r0RFWaWr28tKGdaWsyg5qLyB/64dgbXX0sQI61jyjAMKioqxHmDd+6FFY38eksh/9h/3pPMtyAzgQdWTmbFNGuWfx/pbR6MSPWG4Hafmx7Pu8fKqWtx9jIZm0Gk3kS3jNEOKhraGBsfwcxx1vVOXzNjDBfqWth9pobMpMiL8tl6utc0t1PR0MY1M8awMLP7lBdxkSFcP2cs188Zi2EYHC9rZFtH4JJ3uprztS28klfEK3lF2G0al2TEe3JjZo2L6/Ufp8rGNjYcLiUixD6IFcf7bvPeSIoK5WRFI28cLOGBlVMCvg5XV/zx+VaBi0IUR0rq+PWWQtYfvkDn38MVk5O5f+VkLpuYGPSzpSoUEhgbF86McbHsLKwiJjzEq0qbVqebhjYn18wcY2luRXiInbsWZRBit7HrTDU2TWNMbLhnkkIwL6Y1Le1UNrQR4rBx3aw01swe228PraZpTEuLYVpaDF+8ciLN7S52nqrmveMVbDteYU43cKaGXWdq+NnG4yRFhbJsSjJXTjWrtVJizCkXDhTXUlbfOqRJ8rxF0zQyEiIpLG+ioLQhoEuWBAJxgcuvfvUrfvrTn1JaWsrcuXN5+umnWbSo7+7tdevW8Z//+Z+cOXOGKVOm8Pjjj7NmzZoAGit8wZ6zNfxqy0k2Hyv3bLt6+hgeWDWZeenx1okpFCMQTdP4+JyxlNa1cLK8gUkp0f0uStnqdHO6son5GfEsm5IcQNPeiQkP4VOLM5g5Lo6801WcLG/kXE0LIZpOdgycLG8kMjyMRdnmwpQzxsYOelg5MtTBypxUVuakAuawdWcQs6Owiqqmdl7fX8Lr+0sAcwj78snJlNQ0ExVmx+bnf7LCQuwYhsGus9XMGu/bZTysRlRy7p///Gc+85nP8Nvf/pbFixfz5JNPsm7dOgoKCkhNTe2x/44dO7jyyit57LHH+PjHP87atWt5/PHH2bt3L7NmzfLqNTsTi2pqaoiPj/fxGfkPXdcpLi4mPT09KKsW+uJkeSPHS+ugsYq5OZM4U93CM5tPknvKLK+0aXD9nHHcv3ISOWnBlSwttc1BrrtUb5Dhfq6mmbU7izhZ3khseAipsWGE2DRi3LU02ONpcemU17fS6tSZlxHPXZdmBN1cOrpucLqqiaKqZlranRiNVUQljWFaWixpseF+uaA73Tp7z9aYgcyJCg6fr+/2eIhdIz0hkswkcxK8AcugDcPT5gzCt6a5nVanm/+4LmfAZRv8RW1tLQkJCaN3yv/Fixdz6aWX8swzzwDmH356ejoPPvggjzzySI/977jjDpqamnjzzTc92y677DLmzZvHb3/7W69eszNwKSkpUVVFfuZISR2v7CwyF+FrdnGhwUllk5nY5bBp3DBnDF9YmkGmsHWEFArJ1DS1s+dsDbvOVFFW345hGBiYs+o6bBoZSZEszEpgQabvSoVHGpWN7eSequZv+y+w/1w9Tnf3y25cuIP0hDDS48MZF9v3cheDxa3rnKls5r4VE5kx1prhovr6esaNGzc6q4ra29vZs2cP3/nOdzzbbDYbV199Nbm5ub0+Jzc3l29+85vdtl177bW8/vrrfb5OW1sbbW1tnt/r681Iedw468vKRjJaWBTh6bMIz5xLRPYlhCSZpZS6s43Ggxup3/kaTzRU8ITFngqFQjE8NELHTCQ8+xIiJi4gbPx06lqh7oKLwxeaMFzttJ47QsupvbSe3ouz8uywX/E5H1gHE2ICl8rKStxuN2PGdF8fY8yYMRw7dqzX55SWlva6f2lpaZ+v89hjj/Hoo48OX1jRL5ojjLAJ0wnPmEt45lxC0yahdVn0TG9rpmHfeup3vY7eXGudqEKhUPgUg/ayQtrLCqn/cB1aaKTnH7aIiZfgiBtDRNZ8IrLmA5/H1VBJ6+m9tJzaQ8vpfRjtzVafgOWICVwCxXe+851uvTT19fWkp6dTWFhIYmIiYPb02O123G53twWkOre7XK5uJWB2ux2bzdbndqez+wyUnXM6XFz/3tf2kJAQdF3H7XZ7trlcLt59912uueaabuPnmqbhcDj6dPfXORmajcMlDeSeqibvbC0Hzjf06C5NjQ4hzA7jYhx8LKGRffM+z12/eZScsbHd3C8+V6vO6eL3w+l0snnzZlavXk1ISIhX71OwnNNA7oP57AXynNrb29m8eTOrVq0iPDzcb39P/jin3twD+R0x1HNqbW3lX//6F6tWrSIkJCSovvcGOqe+3AP1HfHUu8epa3GREmsuI6DRfakE3bBR1+qiuKaFoto2SurbISaZ6DmriZ6zGpsGaTGhZCaEkZkYTlx4CJpmoHUrk9YwsHUc2+BMRTOXZidw68IMS96n+vp6MjN9u9q2mMAlOTkZu91OWVlZt+1lZWWkpaX1+py0tLRB7Q8QFhZGWFjPJKa4uDhRyblOp5Pw8HBiYmIsWcNF1w3yS+vZcbKKHYWV5J2upqnd3W2ftNhwlk5O4vJJySydnERydBhbjpWz70wlCa4G7po5lcWTrJmTZSh0tnl0dLS4dXOkund6x8fHi/IGue6hoaEivcF697CoWGx6G2ERfa9/FB4JYxLjWIg5w+752haKqpo4X1lLWYtGSX07JfXt5J5tIC4ihOzkKLKToxgfH9Fr2boWBtGxcZblaHYNIH2FmMAlNDSUBQsW8O6773LTTTcBZnLuu+++ywMPPNDrc5YsWcK7777LQw895Nm2adMmlixZMujXt2rZ+aFit9tZsmRJwLwNw+B0ZRM7Cs1AJbewiprm7pF6QmQISyYlsXRSMksnJZGd3HO67NUz07h6eiqVlakkJyeLCVog8G3uS6S6S/UGue5SvcF699hwByW1LV7v77DbyEyKIjMxkohJkVxoDeVMdTOnK5s4X9NCXYvTs/BlqN1GRmIk2clRZCV/NPGebtBtDptA44+2FhO4AHzzm9/ks5/9LAsXLmTRokU8+eSTNDU18bnPfQ6Az3zmM4wfP57HHnsMgK9//essX76cn//851x//fW8+uqr7N69m9/97neDfu1gLVfsC5vN1muJuC+5UNfCjpNVbO8IVC7UtXZ7PCrUzqLsRDNQmZzE9DTv5koIhLs/kOoNct2leoNcd6neYL17emIkB4rrBv9ETaPFHk18FMyLCmVeejztLp2ijiDmTFUTze1uTlY0crKiETB7tLOSIrFpGinRF89+HDj8ce0UFbjccccdVFRU8F//9V+UlpYyb948NmzY4EnALSoq6tZIS5cuZe3atXz/+9/nu9/9LlOmTOH111/3eg6Xrlw8zhfsOJ1ONm7c6MlZ8AU1Te3knjJ7VHacrOJUZVO3x0PtNi7JjGfppGQun5zEnAnxQyrr84d7IJDqDXLdpXqDXHep3mC9e1rHwo+9rQHVH5rhJrPlJGcjJmNoZg9GqMPG5NRoJqdGYxgGZQ1tnKls4nRlE+UNbZTWt1Jab/4zeeLlRq6eMYarclK5fHIyEaH+7XEqq2/lQHEt52paqKqu8vnxRQUuAA888ECfQ0Nbt27tse22227jtttu87NVcDLcxa2a2lzkna5mR2El209WkV9aT9dZf2wazB4fx9LJyVw+KZkFmQk++4OQtqhlJ1K9Qa67VG+Q6y7VG6x1z0iKJDbCQU2zk8SowfWC2C5K5O2KpmmkxYaTFhvOZROTaGxzcaayiaMX6qloaKOisc2zplKYw8bSSUms6pj1d0KCb+bFMgyDoxfq2XO2hsPn66htdhLmsEGb76ugxAUuVlFU1URSUpLVGn6lzeVmX1EtO05WsqOwiv3FtZ4FDDuZOibak6OyeGLSwDM+KhQKhQKA1JhwZo+PY0dh1aADl8EQHeZgxrhYHHaNW+ZPICzUzub8Mt49Vs65mha2FFSwpaAC/nGEnLQYVuWkctX0VOalJ3i1LtXFON06bx8q5d1jZbS7dFKiw8hJM2clbmscxcm5VvPll/czd1I58ZEhxIaHEBcRQlyk+TM2PITYiI5tESHERjgIcwR/4ppbNzh8vs6To7LrTDWtzu5RfXpiBJdPSmbJpCSWTEoiNSbcIluFQqGQz4LMRHadqaax1UV0uP8uwRUNbSRGhnJJVgLJ0WEsn5rCD28wOFHeyLv55Ww+VsaeszUcK23gWGkDv95aSEJkCCumpbIqJ5Urp6Z49Y+pWzd4fd95Nh8rJzk6zK8BWSeipvy3gs4p/9Mf+gu2MO+71MJDbB8FMuFdg5qLgpxwR48gKDLUPuxqmsZWJ2XVtYxLTiCiI7vcMMwP7Y6TlWwvrOLDU1U0tHbvNk2ODmPppCQun2xW/6QnBn56fcMwaGhoICYmRlRVkVRvkOsu1Rvkukv1huBwd+sGL314htzCKqamxni3uKNhEGK049RCvVqrqM3l5kxlEzfPH8+1s8b2uV9NUzvbTlTwbn45WwvKqe9yPbDbNC7NSuCqnDGszEllUkrPKlCATUdL+fu+ElJjwnoNdFobG/juJxeO3rWKrKAzcPnOKx8yPSuN+hYn9S1O6jpu9a0f3a9rdtLQ5mK4LRpi1zzBTkyXICcuwtEtCIq7OAiKCCEmzMGJ8kb+nHeWhpZ2IsJCSE+M4tD5OnYUVlHZ2NbttWLCHVw2MYnLJyWxdHIyU1KjLf8yMgwDl8uFw+Gw3GUwSPUGue5SvUGuu1RvCB73ioY2frXlJLXN7WQmRQ38BMNAQ8fANmDgYhgGJ8oamT4ulvuunOj1+lEut86eszVsLihnc345J8obuz2emRRpDinljGFRdiKhDhs1Te385J1j6DqkxfXeG68CFwvoDFwqKipITh54uXZdN2hoc3mCm65BTvdAx+V5vOs+F+eUDBZNgxC7DYcGoZqb2vbuH/LwEBuXZiWyZJI58dvMcbH9LldvBU6nk/Xr17NmzRpRVQtSvUGuu1RvkOsu1RuCy33P2Rpe/vAsDrvG2Li+J6QDs6oou+U4pyOmeqqKeqNzPq2EqFA+f0W2d0FRHxRVNbP5mJkXs/NUNe3uj9IIosMcXDE5mQkJEZysaGT2uLg+e47aGuv4zicXjc5FFq3G2+jcZtM8PSDpg3wNwzBocbq79eDUt7q6Bz799Pi0OnUMA9pdOu1AMxoaMC4+glsuGc/SycnMz4gXkX+jUCgUI5lLMuJpdbr5255zFFU3MyEhAtsweoFcbp3TVc0kRYVw9+LMYQUtYFZA3XN5Nvdcnk1Tm4sPTlayOb+czQXlVDS0seHIR2v+5V+o98zgmxId5vfeLBW4BBGaphEZ6iAy1DFgBN4bbS439S0u/rKriB0nKrgkooLTtnHcuTibK6YM3FukUCgUisCgaRpLJyUR6rDxj/3nOV7aQHpiJFFDmOW2prmd0rpWslOiuG1BOpNTo33qGhXm4NqZaVw7Mw1dNzhcUse63cW8ceACtS1OyurbKKtv48NT1USF2clOMoMYf+VIqsBlBBHmsJMSY+cLV05k1thoao9XsGxWJguzR3YZt0KhUEhE0zQuzUokPSGSNw6UcOBcLYYBqTFhxIT3n4ejGwY1Te1UNbUT5rBxzYwxXDsrjdhw/w6B2WwacybEoxtQ2djOuLhwzlQ3c6ayiaLqZpra3BwuqedwST12m8bYCN+XQ6sclwHozHGpra0lLi7Oah2vCZYktKEg1V2qN8h1l+oNct2lekNwu7vcOgfP15F3uprjZQ00t7mx2SAy1EGYQ8OhgVOHVqdOi9ONYUBchIPZE+K5NCuRqWMCW1ix60w1z39wmqljYrqdw/naFk53zOBb3+pCb2um+MnbVY6LwjtaWlqIiYkZeMcgRKq7VG+Q6y7VG+S6S/WG4HV32G1ckpHA/PR4iqtbOFXZSElHENDc7sKht+PSQhkbH0F2ciRj4yKYMibasrm1Opdz6bp8gWdRyKQolk81qG5q53hxOcU+fu3gKicJYqRNce1yudiyZYs4b5DrLtUb5LpL9Qa57lK9QYa7pmlkJEWyYloqn1qcyXfXTOe7101ljv08371uGv++eiq3Lkjn8snJlk4IGh1mJ8Su0erqfSkCTdNIig7jknTf9LJ0RQUuCoVCoVAEKZqmEer4aGHFYBniykyKYkJCJOUdCzkGEhW4KBQKhUKhGBQhdhuXTUyixanjHub8Y4NFBS4jGIdDbgqTVHep3iDXXao3yHWX6g1y3YPRe86EOJKiQqhoaBt4Zx+iqooGoLOqyJcZ0QqFQqFQjAT+dbSU1/adJzUmvPe1ipoa+e7NC3x6DVU9Ll6i670nIAUruq5TXl4uzhvkukv1BrnuUr1BrrtUbxDo3t4EVYXopYcpz/8QvfQI1JwFV2B7OPpjZc4YVuWkUlbfSlVjL15+6BtRgYuXuN2+n0THn7jdbnJzc8V5g1x3qd4g112qN8h1l+oNAtx1HSpPwqG/wdb/hXe+B1sfw73tCXKPl+He9gvY8mPY8B14/xdwbD3Ul/glOPAWu03jpnnjuX72WBrbXBwva6C6qZ3OwRwN3weJwTdoplAoFArFaMLtgvN74Mz7UFEAzmYIjYbQKIgZB7ZwaAISJ4G7Gdoboeyo+Zxjb8HYOZB1BaTOGHD1aH/gsNu4fs5YJqVGs+dsDQfP1XKstJVQhw1am33/ej4/okKhUCgUCu+ovwBHXoNzuwAbxKSZQUvXAMToGByx2cEeZQY00WPMnpaWGjizHc7thokrYfrHITzw+ZiapjF9bCzTx8ZSXj+GA+dqKaltoarK971BKnDxkmCpnfcWTdOIiYkR5w1y3aV6g1x3qd4g112qNwSZu2HA2R1m0NJQCgnZZkDSCxoGMbZWNC4KAjQNIhPNW3M1FLwFlQUw9y5IzQnASfROamw418xIA6C6Oo6HfXx8VVU0AKqqSKFQKBQ+xTDgxCY4tA40G8RnmD+Hi+6CqkKIiIcF98C4ecM/5jDxxzVUJed6iZgs9A50Xefs2bPivEGuu1RvkOsu1Rvkukv1hiByP7UFDv4FQiIgIWvAoEU34Gy7uSJzv9gckDzVzIHZ8wcoO+Ir4yHjj7ZWgYuXBG0Weh+43W72798vzhvkukv1BrnuUr1BrrtUbwgS94rjZtWQIxxixnr1FDc29reOw+3NJVvTIGEitNbBvj9BU9UwhYeHP9paBS4KhUKhUASC9mY4/DezRyR2nP9eR9PMCqTaIjj6ullmPYJQgYtCoVAoFIHg5L+g7LCZiOvvBGGbHeLS4ex2s2x6BKECFy8Jiiz0QaBpGikpKeK8Qa67VG+Q6y7VG+S6S/UGi91bauHUVohIAkfYoJ6qYZBib+xZVTQQ4XFgAIXvgm7N8Jg/2lpVFQ2AqipSKBQKxbA5tRV2PQfJOWZvSKBob4SGMrjy3y0pkVZVRRYiLRHN7XZz7Ngxcd4g112qN8h1l+oNct2leoOF7rrbnCTOHj6koMVtaBxrTcFtDKH3IjQa3K1QnDf45/oAlZxrIZaXzw0SXdcpKCgQ5w1y3aV6g1x3qd4g112qN1jo3lgOdecgKmVIT9fRKGhPQWeIwy7hCVB+xFxaIMCocmiFQqFQKKTRcAGcTX3OjOt3wqKhtR6aKqx5fR+jAheFQqFQKPxJY5k5W24gc1u6EhJlBk4NF6x5fR+jAhcvsdlkNZXNZiMjI0OcN8h1l+oNct2leoNcd6neYKF7UxUMdZgHsOkupjqPYHO3D/EAdrO6qLVuyA5DxR9traqKBkBVFSkUCoViWOx6Hs5sg+Rpg39u9Sk4uclcBVqzQ+xYiMsw1zeKHQ/2EO+OU34ULvkMTLlm8A6DwTDMnp36Emgopb62mriln/XpNVStDu0l0jLo3W43Bw8eZM6cOdjtFnVPDhGp7lK9Qa67VG+Q6y7VGyx07+zxGAytdeb8K5XHATA0G5rhNpN8685B0Q5zjaOYcWYQE5cOcePBHtrHATX/Tnqnu+HCfnPF6/J8aGsAwN3s+4RgeX19FiEtg17XdYqKisR5g1x3qd4g112qN8h1l+oNFrqHRIDh5T+/usu8+O96tiNo0XCPX8T62b/BeemXYerHIHUmhMWAoUN9RxBz6M+w/UnY9xKceg+qT0O3oSUD7IOb+M5rGsog73ew4xk4txtCYyAlB1JnoCf7fu4Y1eOiUCgUCoU/iU41h1AGouuwEJi9KFNWo0eOwdUQARGJEBkPY+eax2utg9qzUFdsrkvUVg/1581bca7ZIxOdZq6LZA/zT1VT5QnY/bz5+vGZZkDlZ1TgolAoFAqFP4kZaw7huNp6n+7/omEhQqNh4kpInWEO7/QW82gaRMSbt7FzO45TawYQtUVmMNNaBw0l5g3guath3DzIvByylkHGZRA+jLyT6tOQ96w5T03qDDNQCgAqcPESaRn0NpuNadOmifMGue5SvUGuu1RvkOsu1RssdI9JM3si2urB0WUSOt1lzmhbtMO8jwYTFkLmFd0CHBsG00IrsA2UKBMeD2nxkDbH/L21FmqLzcTclmozkDm/x7zteMoMNMbOhawrzNfMXGKub+QNzhY48KqZiJuS02fQMqDzEFBVRQOgqooUCoVCMWx2/d5cryh1hvl7H8NCQ51dt08M3QxcFtxjBhhnt8OZD8xbzenu+2o2SJtt9sZkXQEZS8wend44+gYcfBWSpvS7aGR9Uytxn/hvtVaRFbhcgZ8qeTi4XC527Nghzhvkukv1BrnuUr1BrrtUb7DYPX2xWbrcUApHXoNDfzGDltBoyPkEzP1Un0GLy9DY0ZSBayhrFTVVQmQSjJsP8ekw90648Rn4+n74xlH45LNmmXTiJDPIuXAAcp+BV+6Ex7Pgt8tgw3fg2FvQXG0es7W+Y6XrxAFXuh6S8wCooSIvkdYxZRgGFRUV4rxBrrtUb5DrLtUb5LpL9QaL3ROyzVyQs693VBj1PizUGwYaFe5oDDQGVVdt6NBUBlOug6jkno/HjYc5t5s3gPoLHT0y75s9MlUnofSgefvw16bzmFmQmG3O05K+eGCFYUy81xcqcFEoFAqFwp+c+Be8/S1zeAjMZN1pa3w/LHQxdcXma01d7d3+sWNh9q3mDczeoTMffDS8VHkcyg6ZN4Dzu81z8MwjkwGhkf45ly6owEWhUCgUCn9QW9QxzPKm+Xt0mhmwNJaZibT+pL3RTKCde5eZHDwUYtIuCmTKzADmg198VLXUVGHezu8x94lMNgOZzmAGL2f2HQQqcPESaTNE2u125s2bJ84b5LpL9Qa57lK9Qa67VG8IoLurzaza2fZzcLWYU/Vf9hVY/h/m49t/CZUFkJzj1cKLdnTmhZdgx8uJ81ytUHPGTLDNumLo53ExMWNgytVweqtZUm0P/WgOmdoiaK786FayFwCHLdF3r9+BqioaAFVVpFAoFAqvuXhYKPMKWPNTGDPjo33qS+DD35hVPcnTwObDPgRnC1SfhPELYdEXfT8hXHM1bPovc8XpiyuO2puhruijYKapgvo2g7j/bVBVRVYgLYPe5XKxefNmcd4g112qN8h1l+oNct2leoOf3WuL4NW74eVbzKAlOg0++Rzc82b3oAXMmWwXfxmSpkLFsQFXbXYZGpsbJ/ZfoWMY5hBUdSGkXwaXfsE/s9jaQ80eJL2XNgyNNEuuJ18DCz8PS7+Ge9onfK6ghoq8RFrHlGEYNDQ0iPMGue5SvUGuu1RvkOsu1Rv85N7fsFB/s9LGjYelD8DRf5h5I02VZl5IL6s9G2g06OF9VxU5W6H2jDml/+zbzWTckAifnWI3QqPMhNy6c71XKnUlJBI9aarPFVTgolAoFArFUPBmWKg/IhPhks+aJcZHXzeHeDSHmRQbGt3/as6GYc4D01RuThyXkgOzboEU3wcK3dA0SJ5s5uhYhApcFAqFQqEYDD2qhcbA6h+b1Tf9BRu9YbNB+qVmsHPhoFl6XHnCzBMBcESAPRy0HHPxRFcTuNsADcJizdltMy4zZ+R1hPr0NPskdQYcf8fMp/FXz04/qOTcAehMzq2pqSE+Pt5qHa/RdZ3KykqSk5PFrSki1V2qN8h1l+oNct2leoMP3Ic6LDQ4SXPF54YL5oRwNafQWxuodEWR7GjGFpVoTmYXMxbiJpi9M4MNloaL2wlbH4Oas5A0ud9daxtbSbjBt1P+q8BlAFRVkUKhUCiGPSw00ji326yMikrud04atVaRhTidTqsVBoXT6eStt94S5w1y3aV6g1x3qd4g112ct2GYc5qceg9n/gbeeuMfOAu3mdPve0OPaqExfVcL+YmgbPPxC8zFGOvOmfPG9IHT8H2YoXJcRjASyxU7keou1Rvkukv1BrnuIrxdbVB6CM7uMFdHbmsEWyiusBth9wsQHg3jL4H0RZAy3cw1ufj5/h4WGszpBFuba5qZ09NaY/a+JGSZCcUBQAUuCoVCoRhZtNTA3pc6pqHXzDyQuAzADg1A8lRoqYTCzXBmO0xaaV6EOxc7PPEvePvb5pwooIaF+iIs2pwvxhEORR+aibqxE7yaDXg4qMBFoVAoFCOH1jrY+TtzRePESd2rXjozOjWbmZsRlQwttVDwtlkhM2kVbPpP31QLjRbCYmDRfTBmJuS/AZXHzCqoiHhzzpf+Js0bIio5dwA6k3Nra2uJi4uzWsdrOidaiomJQRP2ByfVXao3yHWX6g1y3YPaW9dh52/NCd1SppmzvHbBMKBBDyPG1tY9DmmpNdffqT4F7nbLh4UuJqjbvCutdVCyH4pyzWUN2puoa2gk/t6/+DQ5V/W4jGAiIgJfX+8rpLpL9Qa57lK9Qa570HpXnYSSfZCQ2SNo6STCdlGCa/UpOLnJHF4Cc7r8jz8RdMNCQdvmXQmPg4nLIftKaGswlyCoKgf+4tOXUVVFXhJ0iVED4HK5WL9+vThvkOsu1Rvkukv1BrnuQe19bpc55BPW+3/2Lmysb8jBhc3sHTjyGhz6ixm0hETB2PlmL0uQBS1B3ea9oWlmT1XyFFzJOT4/vOpxUSgUCoV8mqvhXN6A6+fYdCe2ou1QtL1joUANJiw0E3Drz5sz105c7vcEU8XQUYGLQqFQKOTTWA6t9eassn2gVZ9i5YnfY28rMzfEpcOU1eaigQARCeZx2hrM5FJFUKICF4VCoVDIx91m9qDY+risnduFo/BdogEjNApt4ipzzZ2uya42h3kMV1tAlBVDQ0xVUXV1NQ8++CBvvPEGNpuNW265hV/+8pdER/c94c3vfvc71q5dy969e2loaBjSekOSq4pcLhcOhyO4s9B7Qaq7VG+Q6y7VG+S6B6132RF47yeQNKX7MI9hwJn3oWgHAO60edgmrkQLCet5jNY6s7flmv+GqKQAiQ9M0La5F9TV1REfHz86p/y/++67OXLkCJs2beLNN99k27Zt3Hffff0+p7m5meuuu47vfve7AbIMLlpaWqxWGDJS3aV6g1x3qd4g1z0ovUOjzYnQnM0fbTN0OLHRE7QYmctomnTDRxPNXUx7sznvS2hUAIQHR1C2uUWICFzy8/PZsGEDzz33HIsXL+aKK67g6aef5tVXX6WkpKTP5z300EM88sgjXHbZZcN2EJPN3YHL5WLLli3ivEGuu1RvkOsu1Rvkugetd1y6uVJxY6n5u+6G/H/ChX3m71NW48pcxpbmyWZV0cUYBrRUmUsAhIQHztsLgrbNvcAfziJyXHJzc4mPj2fhwoWebVdffTU2m42dO3dy8803++y12traaGv7aHyzvr4eMBe56lzgymazYbfbcbvd6Lru2bdzu8vlousInN1ux2az9bn94oWzHA7zbbn4De9re0hICLqu43a7Pds699F1vdvxNU3D4XD06R4M59T53L7cLz7XYDmnzscNw/B07Xalt/cpWM5pIPfBfPYCeU5d/f359+SPc+rNPZDfEcM5p07vi8/J6u89LX0pjtKD6G3NUPAGtprTGJoNPecG7Kk5uHVzmKVz4T8bBnbNwG1o6K1NEBoHY+Zhc7uD55y6DA1J+y73FyICl9LSUlJTU7ttczgcJCYmUlpa6tPXeuyxx3j00Ud7bN+yZQuRkZEAZGRkMH/+fA4ePEhRUZFnn2nTppGTk0NeXh4VFRWe7fPmzSMzM5Nt27bR0NDg2b5kyRJSU1PZuHFjtzd75cqVREREsH79+m4Oa9asoaWlhS1btni2ORwOrr/+eiorK8nNzfVs78z9OX/+PIcOHfJsT0lJYenSpZw4cYKCggLP9mA7J4Cqqip27drl2R4TE8OqVasoLi5m//79QXtOLpeL1tZWr96nYDunxsZGYmJihvXZs+KcNm3a5Ne/J3+e06ZNmyz5jhjqOR05csTjPdj3ye/nFB3FqqgUnAf+QlhLKS5bKHnZX4OoiSyliMJ2M29lU+NU85xCapgfcYGDLWMock0HO/BhPtOm6cFzTjExLFu2rFube/M+Bct3uS9GPC7G0uTcRx55hMcff7zfffLz83nttdd48cUXu705AKmpqTz66KN85Stf6fcYW7duZeXKlV4l5/bW45Kens6FCxdISjI/9MEW0fbV47J582ZPz1QnEqJ0p9PJli1b+nS3uneiL3en08nmzZtZvXo1ISEhQdc7MVCPS3/uVvdO9HVO7e3tbN68mVWrVhEeHh6UvRN9nVNv7hJ6XFpbW/nXv/7FqlWrCAkJCarvPa3hAo6XPgE1ZzDsobhn3YkRNwENA4dm0Krb+VfjZFZFFxKi6WaPi+HCXXkSPS4dFn0JYsYE1zlpGoZh8M4773javHN7MHzvDbS9vr6e5ORknybnWhq4VFRUUFVV1e8+EydO5E9/+hMPP/wwNTU1nu0ul4vw8HDWrVs34FDRYAKXi+msKvJloysUCoXCx1SehJduhroiiEyCrGVmcm7MOHMq+ourcQwdmiqhqcxcjHHh5yBxojXuIxh/XEMtHSpKSUkhJSVlwP2WLFlCbW0te/bsYcGCBQBs3rwZXddZvHixvzUBukWuEtB1ncrKSpKTk7v1WkhAqrtUb5DrLtUb5LoHpfeFA/DSJ6G50gxC/u3v4HbC8Q1mmXT9OQiNQbeHUkkCya4L2JxNZoAz5TqYei3EjLH6LPokKNvcS/xx7RTRAtOnT+e6667ji1/8Inl5eWzfvp0HHniAO++8k3HjxgFmLkfn+F0npaWl7N+/n5MnTwJw6NAh9u/fT3V19aAdunbdScDtdpObmyvOG+S6S/UGue5SvUGue9B5n34fXrjeDFrS5sC975iLLCZPhiX3w8rvwqxbIX4C7tAYcrkEd+IUWHAPXPVfsOAzQR20QBC2+SDwh7OI5FyAl19+mQceeICrrrrKMwHdU0895Xnc6XRSUFBAc/NHNfy//e1vuyXaXnnllQC88MIL3HPPPQFzVygUCoUfOPYWrPucOWtu5hVw11pzWKgTTYP4dPPGzeB0wvr1cMVD0JEropCHmMAlMTGRtWvX9vl4VlYWF6fr/PCHP+SHP/yhn80UCoVCEXD2vQz/fBAMN0y7Hm59PujmX1H4BxFDRcGAtGmWNU0jJiZGnDfIdZfqDXLdpXqDXPeg8N7xNPzjq2bQMu9uuP2PXgUtQeE+BKR6g3+unWLWKrIKVVWkUCgUQYJhwLuPwgdPmL8veQCu+REIS1gdTfjjGqrebS+RWFV09uxZcd4g112qN8h1l+oNct0t89bd8MbXPwparv4hrP5/gwpaVJsHnlFbVRQMSMvmdrvd7N+/X5w3yHWX6g1y3aV6g1x3S7xdbbDuHtj7Img2+MQv4Ypv9JybZQBUmweeUV1VpFAoFIpRSFsDvHo3nH4P7KFwy3Mw40arrRQWogIXhUKhUAQnTVXw8q1QshdCo+HOl2HiCqutFBajAhcvkZbNrWkaKSkp4rxBrrtUb5DrLtUb5LoHzLvunDmFf+VxiEiET/8Vxi8Y1iFVmwceVVVkAaqqSKFQKAJMxXEzaKk/B7Hj4d9eh5SpVlsphoCqKrIQaUlRbrebY8eOifMGue5SvUGuu1RvkOvud+/ze+H5a82gJWmKOYW/j4IW1eaBxx/OKnDxEmllaLquU1BQIM4b5LpL9Qa57lK9Qa67X71PvQcvfgJaqmHcfLh3Q8d0/b5BtXng8YezynFRKBQKhfUc/Sf87fPgbofsK+HOtRAWY7WVIghRPS4KhUKhsJY9L8K6z5pBy/RPwKfWqaBF0ScqcPESm7AppW02GxkZGeK8Qa67VG+Q6y7VG+S6+9z7gyfhja+BocMln4HbXvTbYomqzQOPP5xVVdEAqKoihUKh8AOGAZv+01wwEcyZcK/6waBnw1UEN6qqyEKkZXO73W727dsnzhvkukv1BrnuUr1BrrtPvN0u+McDHwUt1/zIXHvIz0HLqG5zi1BVRRYiLZtb13WKiorEeYNcd6neINddqjfIdR+2t7PVzGfZ/ydz3aEbfwWXf823kn0watvcQlRVkUKhUCjk0loPr34KzrwP9jC49XmY/nGrrRTCUIGLQqFQKPxPYwW8fAtcOAChMXDXK5C9zGorhUBU4OIl0rK5bTYb06ZNE+cNct2leoNcd6neINd9SN61ReYU/lUnITIZPv03GDfPb459MaraPEhQVUUWoKqKFAqFYhiUHzODloYSiEs31x1Knmy1lSJAqKoiC3G5XFYrDAqXy8WOHTvEeYNcd6neINddqjfIdR+U97nd8MJ1ZtCSkmOuO2Rh0DIq2jzI8IezGiryEmkdU4ZhUFFRIc4b5LpL9Qa57lK9Qa67196Fm+HVT4OzCcYvhLvXQWRiYCT7YMS3eRDiD2cVuCgUCoXCtxz5O/zti6A7YeJKuONPEBZttZVihKCGihQKhULhO3Y/D+s+ZwYtM2+GT/1ZBS0Kn6ICFy+x2+1WKwwKu93OvHnzxHmDXHep3iDXXao3yHXv09swYNvP4M1vAAYs+Bzc8ntwhFni2Rsjrs0F4A9nVVU0AKqqSKFQKAZA12Hj9+HDX5m/X/ktWPk9te6QQlUVWYm0bG6Xy8XmzZvFeYNcd6neINddqjfIdXe1t7P53XdxOZ3mBrcT/vHVj4KWax+DVd8PyqBFbJsL9QZVVWQp0jqmDMOgoaFBnDfIdZfqDXLdpXqDQPfWOji7A+NMLg2tCzC2/RzSL4Fdz8GJd0Czw02/hrl3Wm3aJ+LavAOp3qCqihQKhWJk0FoHJYfM+652CAmx1mcgmqth5/9B2WEISzD76iuPw57fQXMVOMLhthdh2nVWmypGASpwUSgUikDS3gS5v4Hze0lmPtqWnTDzBnCEgM0Bts6fdrB33r/o1rk9UMMxJ/8FZYcgeRrYwgmrrsNR9L4ZtNgccPUPVdCiCBgqOXcAOhOLampqiI+Pt1rHa3Rdp7KykuTkZHHrW0h1l+oNct1Fepcegtfvxyg9iMYwv341W5dAxwH2XoIcT6BjH0Rg1OW+YUBxHmBAWCy6ZkMvO4qjtRpCoiBjCaQvghX/4ZPm8SciPy/I9Qaora0lISHBp8m5qsfFS6R9WGw2G6mpqVZrDAmp7lK9Qa67OG+3C3KfgdIDaGBe+O0OCE8Aww26y7y5naB3/u40f/aGoYO7zbwFCFvHjfA4mHMnuNqgqcKsLAry70lxn5cOpHqDf66dKnDxEmdnBr0QnE4nGzduZPXq1YQE+/j5RUh1l+oNct1FebfWwV/vNYddAPf4xWxI+RKrF2QRkr6g/+caRkeQ4vwouOl66y3Q0d2+2b+9EQrfAwwICcet65x2jyUzezIh4ZFQfRqikoM+aAFhn5cuSPUG/1w7VeAygpFYOteJVHep3iDXXYR3zRlYewdUHANHBHzy/9AnXoPrnU2QNmfg52uaWbVjs2gCsj0vmpVDKTPQtRCONOSQGXYMXM3mLXOJNV5DQMTnpRekevsDFbgoFAqFPzmbC3++20xkjRkLd70C4+aDpF7cKauh6iSUH4WosUAO1J+H1ioYvwDSL7PaUDGKUIGLQqFQ+IsDr8I/HwR3O4ydC3e9CrHjrLYaPLFjYcn9cPJdKN5rbguNhimrYPJVai0iRUBRVUUD0FlVVFtbS1xcnNU6XtM5YVFMTAxaEM5g2R9S3aV6g1z3oPXWddj8I/jgF+bv0z8BN/8fhEZ5dgla9wEwWhtoqK0mJjEFLTTSap1BIbbNhXoD1NXVER8fr6qKFN4RERFhtcKQkeou1Rvkugedd3sT/P1LkP+G+fuyh2Hl93tNXg06d28IiyYiKRwcMi8fItscud7+IPjTwIMEaYlRLpeL9evXi/MGue5SvUGue9B515fACx8zgxZ7qNnLctV/9Rq0BJ27l0j1BrnuUr1BrVWkUCgUwUvJPnjlLmi4AJFJcMfLoqptFAopqMBFoVAohsvRf8Jr94GrBVJyzCTcxGyrrRSKEYkKXBQKhWKoGIaZgPvuf5u/T74abn3enFVWoVD4BVVVNACSq4pcLhcOh0NcFrpUd6neINfdUm9XG7zxdTjwivn74i/D6h+bU/h7gWrzwCPVXao3+KeqSCXnjmBaWlqsVhgyUt2leoNcd0u8myrhxRvMoEWzw/U/h4897nXQ0olq88Aj1V2qtz9QgYuXSMvmdrlcbNmyRZw3yHWX6g1y3S3xLs+HZ1dB8YcQFgef/itc+oVBH0a1eeCR6i7VG1RVkUKhUFjLiX/BXz8HbfWQkA2f+gukTLXaSqEYVajARaFQKAbCMCDvd7DhEXOV5szL4Y4/QWSi1WYKxahDBS4jGIfQmS1BrrtUb5Dr7ndvtxPe/g/Y/Xvz93mfho8/AY7QYR9atXngkeou1dsfqKqiAeisKvJlRrRCoRBCSy2suwdObQE0uOZRWPo1EFbZoVBYhT+uoSo510t0XbdaYVDouk55ebk4b5DrLtUb5Lr71buqEH5/jRm0hETCnS/D5V/3WdCi2jzwSHWX6g3+uXaqwMVL3G631QqDwu12k5ubK84b5LpL9Qa57n7zPvMBPHcVVB6H2PFw7zuQc71PX0K1eeCR6i7VG/xz7VSDZgqFQtGVfX+CNx4C3QnjLoG7XoGYNKutFApFBypwUSgUCgDdDf/6Iex4yvx95s1w028gJMJSLYVC0R0VuHiJtGmWNU0jJiZGnDfIdZfqDXLdfebd1mgukljwlvn78v+A5Y+AzX+j6aO+zS1AqrtUb/DPtVNVFQ2AqipSKEY4dedg7Z1QdgjsYXDjr2DObVZbKRQjAlVVZCHSsrl1Xefs2bPivEGuu1RvkOs+bO9ze8zp+8sOQVQK3PNmwIKWUdvmFiLVXao3qKoiS5GWze12u9m/f784b5DrLtUb5LoPy/vwa/CHNdBYBqkz4IubIX2R7yX7YFS2ucVIdZfqDaqqSKFQKIaPYcC2n8KWH5u/T7kWbv09hMVY66VQKLxCBS4KhWL04GyFfz4Ah9aZvy95AK75b7DZrfVSKBReowIXL5GWza1pGikpKeK8Qa67VG+Q6z4o78ZyePVTcG4X2Bxw/c9hwT1+d+yLUdHmQYZUd6neMMqriqqrq3nwwQd54403sNls3HLLLfzyl78kOjq6z/1/8IMfsHHjRoqKikhJSeGmm27iRz/6EXFxcV6/rqoqUihGAGVHYO0dUFcM4fFw+x9h4nKrrRSKEc+oriq6++67OXLkCJs2beLNN99k27Zt3HfffX3uX1JSQklJCT/72c84fPgwf/jDH9iwYQOf//znh/T60pKi3G43x44dE+cNct2leoNcd6+8CzbA71ebQUviJPjCu0ERtIzoNg9SpLpL9Qb/XDtFBC75+fls2LCB5557jsWLF3PFFVfw9NNP8+qrr1JSUtLrc2bNmsXf/vY3PvGJTzBp0iRWrVrFj3/8Y9544w1cLtegHaSVoem6TkFBgThvkOsu1RvkuvfrbRiw4xl45U5ob4SsZfCFf0Hy5MCL9sKIbPMgR6q7VG/wz7VTRI5Lbm4u8fHxLFy40LPt6quvxmazsXPnTm6++WavjtPZVeVw9H3abW1ttLW1eX6vr68HwOl04nQ6AbDZbNjtdtxud7c3pXO7y+Wi6wic3W7HZrP1ub3zuJ10+l0cYPW1PSQkBF3Xu0W2nfvout7t+Jqm4XA4+nQPhnPqfG5f7hefa7CcU+fjhmFgGIZX71OwnNNA7oP57AXynLr6dzsntxP7hm9j2/+SeV6XfBbX6v8Fewh0PMfqc+rNPZDfEcM5p07vi8/J6u89b86pN/dg+N7r75w6kfZd7i9EBC6lpaWkpqZ22+ZwOEhMTKS0tNSrY1RWVvKjH/2o3+ElgMcee4xHH320x/YtW7YQGRkJQEZGBvPnz+fgwYMUFRV59pk2bRo5OTnk5eVRUVHh2T5v3jwyMzPZtm0bDQ0Nnu1LliwhNTWVjRs3dnuzV65cSUREBOvXr+/msGbNGlpaWtiyZUu3drj++uuprKwkNzfXs70z9+f8+fMcOnTIsz0lJYWlS5dy4sQJCgoKPNuD7ZwAqqqq2LVrl2d7TEwMq1atori4mP379wftOblcLlpbW716n4LtnBobG4mJiRnWZ8+Kc9q0aZPnnLasf435J54gpTEfA4225f+J/fIHWf/220F5Tps2bbLkO2Ko53TkyBGP92DfJ6vPqbCwsJt7sH3v9XVOy5Yt6+bd9ZyC5Xuvr3O67LLL8DWWJuc+8sgjPP744/3uk5+fz2uvvcaLL77Y7c0BSE1N5dFHH+UrX/lKv8eor6/nmmuuITExkX/+85+EhIT0uW9vPS7p6emUlZWRkJAABF9E21uUrus6R48eZdasWd32lRClu91u8vPz+3S3uneiL3e3282RI0eYO3eu5/hdsbp3or9zGsjd6t6Jvs7J5XJx5MgRZs6cSWhoKLbqUxhrb0OrPoURGoX7pt9hn359r+5Wn1MP9yDsnejNvb29nUOHDjFz5kyPX7B87w10Tn25B8P3Xn/npGkaBw4cYMaMGdjt9m7nZPX33kDbGxsbSUxM9GlyrqWBS0VFBVVVVf3uM3HiRP70pz/x8MMPU1NT49nucrkIDw9n3bp1/Q4VNTQ0cO211xIZGcmbb75JeHj4oBxVVZFCIYRT78FfPgOttRCXDne9CmmzBnyaQqHwHyOuqiglJYWcnJx+b6GhoSxZsoTa2lr27Nnjee7mzZvRdZ3Fixf3efz6+npWr15NaGgo//znPwcdtHRFWja32+1m37594rxBrrtUb5Dr3umt73oe/vRJM2iZcKk5fX+QBy3S21yaN8h1l+oNo7iqaPr06Vx33XV88YtfJC8vj+3bt/PAAw9w5513Mm7cOMDM5egcv4OPgpampiZ+//vfU19fT2lpKaWlpUNqSGnZ3LquU1RUJM4b5LpL9Qa57nrNOWJ2PIbtrW+A7oJZt8Jn34To1IGfbDFi21yoN8h1l+oNQVJVtH79eux2O9dee2237e+88w66rvOxj33MZ3Jdefnll3nggQe46qqrPBPQPfXUU57HnU4nBQUFNDc3A7B371527twJwOTJ3csfT58+TVZWll88FQpFgDAMbK9/ickVHYmMK78HV34LBM4uqlAovGfQgcsjjzzC//7v//bYbhgGjzzyiN8Cl8TERNauXdvn41lZWd0Si1asWIGF6TsKhcLfOFuwle4DwHXJ53As/7bFQgqFIhAMeqjoxIkTzJgxo8f2nJwcTp486ROpYMRmEzGq5sFmszFt2jRx3iDXXao3CHU/+g80VyvOiFS01f9jtc2gEdnmyPUGue5SvcE/185BHzEuLo5Tp0712H7y5EmioqJ8IhWMdJagScFut5OTkyPOG+S6S/UGoe57XgAgZMl92MMjLZYZPCLbHLneINddqjf459o56MDlxhtv5KGHHvJM5ANm0PLwww9zww03+FQumAjEbIC+xOVysWPHDnHeINddqjcIdC87CsU7MWwOdrmny/Hugrg270CqN8h1l+oN/rl2Djpw+clPfkJUVBQ5OTlkZ2eTnZ3N9OnTSUpK4mc/+5nPBYMFafkyhmFQUVEhzhvkukv1BoHue/4AgDHlOkrq3XK8uyCuzTuQ6g1y3aV6g3+unYNOzo2Li2PHjh1s2rSJAwcOEBERwZw5c7jyyit9LqdQKBQ9aG+GA68CoF/yWTjWYrGQQqEIJENaq0jTNFavXs3q1at97aNQKBT9c+Tv0FYH8ZkY2cvh2AarjRQKRQDxKnB56qmnuO+++wgPD+82d0pvfO1rX/OJWLAhLSnKbrczb948cd4g112qNwhz70jKZcFnsTtC5HhfhKg274JUb5DrLtUb/HPt9GqtouzsbHbv3k1SUhLZ2dl9H0zTeq04koxaq0ihCCJKD8NvLwebA75xFGLGWG2kUCj6wbK1ik6fPk1SUpLnfl+3kRa0dEVaNrfL5WLz5s3ivEGuu1RvEOTekZRLzvUQM0aOdy9IdZfqDXLdpXpDEFQVOZ1OJk2aRH5+vs9Fgh1p2dyGYdDQ0CDOG+S6S/UGIe7tTXDwz+b9BZ8DhHj3gVR3qd4g112qN/jn2jmowCUkJITW1lafSygUCsWAHH4N2uohIQuyl1tto1AoLGLQ87jcf//9PP744yK7rBQKhWA8Sbn3gMCpzxUKhW/wKjm3KzfffDPvvvsu0dHRzJ49u8c0/6+99ppPBa2mM7GopqaG+Ph4q3W8Rtd1KisrSU5OFre+hVR3qd4gwP3CQfi/ZWALgW/mQ3QKIMC7H6S6S/UGue5SvQFqa2tJSEjwaXLuoOdxiY+P55ZbbvHJi0tC2ofFZrORmppqtcaQkOou1RsEuHcm5U7/uCdoAQHe/SDVXao3yHWX6g3+uXYOOnB54YUXfC4hAafTabXCoHA6nWzcuJHVq1cTEhJitc6gkOou1RuC3L2tEQ7+xbzfkZTbSVB7D4BUd6neINddqjf459o56FBo1apV1NbW9theX1/PqlWrfOGk8BGS85Ckukv1hiB2P/w3aG+AxImQtazHw0Hr7QVS3aV6g1x3qd7+YNCBy9atW2lvb++xvbW1lffff98nUgqFQuFBJeUqFIoueD1UdPDgQc/9o0ePUlpa6vnd7XazYcMGxo8f71s7hUIxuinZDyX7wB4K8+622kahUAQBXlcV2Ww2NE0Dep9QJiIigqeffpp7773Xt4YW01lVVFtbS1xcnNU6XtM5YVFMTIznfZOCVHep3hDE7m88ZPa4zLoFbn2+x8NB6+0FUt2leoNcd6neAHV1dcTHx1tTVXT69GkMw2DixInk5eWRkvJRZn9oaCipqakiF4AayURERFitMGSkukv1hiB0b2uAQ+vM+xcl5XYl6LwHgVR3qd4g112qtz/wesA4MzOTrKwsdF1n4cKFZGZmem5jx44d8UGLtMQol8vF+vXrxXmDXHep3hCk7of+Cu2NkDQZsq7odZeg9PYSqe5SvUGuu1RvCIK1ijp56aWXuPzyyxk3bhxnz54F4IknnuAf//iHT+UUCsUopmtSrrDucYVC4T8GHbj85je/4Zvf/CZr1qyhtrYWt9sNQEJCAk8++aSv/RQKxWikZB9cOGAm5c79lNU2CoUiiBh04PL000/z7LPP8r3vfa/b8NDChQs5dOiQT+UUCsUoZXdHb8uMGyEqyVoXhUIRVAx6raKIiAiOHTtGZmYmMTExHDhwgIkTJ3LixAnmzJlDS0uLv1wtQXJVkcvlwuFwiMtCl+ou1RuCzL21Hn6eA84muOetPvNbIMi8B4lUd6neINddqjf4p6po0D0u2dnZ7N+/v8f2DRs2MH36dF84KXyE5CBSqrtUbwgi90PrzKAleSpkXj7g7kHjPQSkukv1BrnuUr39waADl29+85vcf//9/PnPf8YwDPLy8vjxj3/Md77zHb797W/7wzEokJbN7XK52LJlizhvkOsu1RuCyN0wBpWUGzTeQ0Cqu1RvkOsu1Rv8c+0c9CKLX/jCF4iIiOD73/8+zc3NfOpTn2LcuHH88pe/5M477/S5oEKhGEWU7IXSQ2APg7l3WW2jUCiCkEEHLgB33303d999N83NzTQ2NopdbluhUAQZnUm5M2+CyERLVRQKRXAypMClk8jISCIjI33lovAxDsew3l5Lkeou1RuCwL21zlwJGsxhIi+x3HsYSHWX6g1y3aV6+wOvq4pWrVrl1QE3b948LKFgo7OqyJcZ0QqFohfynoX1/w7J0+D+nWrSOYViBOCPa6jXIdzWrVvJzMzk+uuvJyQkxCcvLgld161WGBS6rlNZWUlycjI225AmSLYMqe5SvSEI3A0D9vzBvL/wc14HLZZ7DwOp7lK9Qa67VG/wz7XT6xZ4/PHHiYiIYN26dWiaxuc//3meeOKJHreRSucMwVJwu93k5uaK8wa57lK9IQjcz++BssPgCIe53if5W+49DKS6S/UGue5SvcE/106vA5dvfetbHD16lNdff52GhgYuv/xyFi1axG9/+1vq6+t9LqZQKEYRnqTcmyEiwVoXhUIR1Ay6z2nJkiU8++yzXLhwgfvvv5/nn3+ecePGqeBFoVAMjZbaISXlKhSK0cmQB8v27t3Le++9R35+PrNmzRrxeS/SplnWNI2YmBhx3iDXXao3WOx+aB24WiBlOqQvHtRTVZsHHqneINddqjf459o5qLWKSkpK+MMf/sAf/vAH6uvr+fSnP829997LjBkzfC4WLKiqIoXCjxgG/OZyKD8CH/sJLP6S1UYKhcKH+OMa6nWPy5o1a5g0aRI7d+7kpz/9KefOneNnP/vZiA5auiKxqujs2bPivEGuu1RvsND93C4zaHFEwJw7Bv101eaBR6o3yHWX6g0WVxVt2LCBxMREioqKePTRR1m0aBGXXHJJj9tIRVo2t9vtZv/+/eK8Qa67VG+w0L0zKXfWJyEiftBPV20eeKR6g1x3qd7gn2un1/O4/OAHP/D5iysUilFMSw0cec28r5JyFQqFl6jARaFQWMPBv4CrFVJnwoRLrbZRKBRCkDUFn4VIy+bWNI2UlBRx3iDXXao3WOBuGB8NEw1iptyLUW0eeKR6g1x3qd4QBFVFoxFVVaRQ+IGiD+H5ayEkEh4+BuFxVhspFAo/YGlV0WhHWlKU2+3m2LFj4rxBrrtUb7DAvWtS7jCCFtXmgUeqN8h1l+oNFk/5P9qRVoam6zoFBQXivEGuu1RvCLB7czUc+bt5f8HnhnUo1eaBR6o3yHWX6g0Wl0OfPn3a5y+uUChGIQf/DO42GDMbxi+w2kahUAjD68Bl0qRJZGdnc++99/LSSy9x7tw5f3opFIqRSLek3HuGnJSrUChGL16XQ2/evJmtW7eydetWXnnlFdrb25k4cSKrVq1i5cqVrFy5kjFjxvjT1VJsNlmjajabjYyMDHHeINddqjcE0L0oFyoLzKTc2bcP+3CqzQOPVG+Q6y7VG/xz7RxSVVFrays7duzwBDJ5eXk4nU5ycnI4cuSIzyWtRFUVKRQ+5G9fhEN/gfn/Bjc+Y7WNQqHwM0FTVRQeHs6qVav4/ve/z6OPPsrXvvY1oqOjOXbsmE+kghFp2dxut5t9+/aJ8wa57lK9IUDuzdVw9B/m/YXDS8rtRLV54JHqDXLdpXpDEFQVtbe3s23bNh599FFWrlxJfHw8X/7yl6mpqeGZZ54Z0Qm80rK5dV2nqKhInDfIdZfqDQFyP/CKmZSbNgfG+WZdM9XmgUeqN8h1l+oN/rl2ep3jsmrVKnbu3El2djbLly/nS1/6EmvXrmXs2LE+l1IoFCMMH82Uq1AoFF4HLu+//z5jx45l1apVrFixguXLl5OUlORPN4VCMVI4ux2qTkBIFMy+zWobhUIhGK+Himpra/nd735HZGQkjz/+OOPGjWP27Nk88MAD/PWvf6WiosKfnpYjLZvbZrMxbdo0cd4g112qNwTAvbO3ZfatEBbjs8OqNg88Ur1BrrtUbwiiqiKAhoYGPvjgA7Zs2cLWrVs5cOAAU6ZM4fDhw752tBRVVaRQDJOmKvhFDrjb4b6tMG6+1UYKhSJABE1VEUBUVBSJiYkkJiaSkJCAw+EgPz/fJ1LBiMvlslphULhcLnbs2CHOG+S6S/UGP7sfWGsGLWPn+TxoUW0eeKR6g1x3qd7gn2un1zkuuq6ze/dutm7dypYtW9i+fTtNTU2MHz+elStX8qtf/YqVK1f6XDBYkLaItmEYVFRUiPMGue5SvcGP7oYBe/5g3vdRCXT3w6s2DzRSvUGuu1Rv8M+10+vAJT4+nqamJtLS0li5ciVPPPEEK1asYNKkST6XUigUI4Qz70PVSQiNhlm3Wm2jUChGAF4HLj/96U9ZuXIlU6dO9aePQqEYSXT2tsy+DcKiLVVRKBQjA69zXL70pS/1CFpmz55NcXGxz6WCEbvdbrXCoLDb7cybN0+cN8h1l+oNfnJvqoSj/zTv+2GYCFSbW4FUb5DrLtUb/HPtHHJVEUBMTAwHDhxg4sSJvnQKKlRVkUIxRLb/Ejb9lzlL7n1brLZRKBQWEFRVRYGmurqau+++m9jYWOLj4/n85z9PY2Njv8/50pe+xKRJk4iIiCAlJYUbb7xxyOspScvmdrlcbN68WZw3yHWX6g1+cNd1vybldqLaPPBI9Qa57lK9wT/XzmEFLsuWLSMiIsJXLv1y9913c+TIETZt2sSbb77Jtm3buO+++/p9zoIFC3jhhRfIz8/nnXfewTAMVq9ePaRFn6RlcxuGQUNDgzhvkOsu1Rv84H5mG1SfgtAYmHWLb47ZC6rNA49Ub5DrLtUbLK4q6o3169f7yqNf8vPz2bBhA7t27WLhwoUAPP3006xZs4af/exnjBs3rtfndQ1ssrKy+H//7/8xd+5czpw5o6qhFAp/0tnbMud2CI2yVEWhUIwsBh24VFVVedYoKi4u5tlnn6WlpYUbbriBZcuW+VwQIDc3l/j4eE/QAnD11Vdjs9nYuXMnN99884DHaGpq4oUXXiA7O5v09PQ+92tra6Otrc3ze319PQBOpxOn0wmYUxjb7Xbcbne3lS87t7tcrm5Rpt1ux2az9bm987idOBzm23JxF1tf20NCQtB1vVtPUuc+uq53O76maTgcjj7dg+GcOp/bl/vF5xos59T5uGEYGIbh1fsULOc0kPtgPntacyWO/DfN4877N+g4tj/Oqau/P/+e/PE+9eYeyO+I4ZxTp/fF52T1954359SbezB87/V3Tp1I+y73F14HLocOHeITn/gExcXFTJkyhVdffZXrrruOpqYmbDYbTzzxBH/961+56aabfC5ZWlpKampqt20Oh4PExERKS0v7fe6vf/1rvv3tb9PU1MS0adPYtGkToaGhfe7/2GOP8eijj/bYvmXLFiIjIwHIyMhg/vz5HDx4kKKiIs8+06ZNIycnh7y8vG5rN82bN4/MzEy2bdtGQ0ODZ/uSJUtITU1l48aN3d7slStXEhER0aNHa82aNbS0tLBly0eJjg6Hg+uvv57Kykpyc3M926Ojo1myZAklJSUcPHjQsz0lJYWlS5dy4sQJCgoKPNuD7ZyWLFlCTU0NO3fu9GyPiYlh1apVFBcXs3///qA9p85uXW/ep2A7p5aWFjRNG9Znb2btJibrTtpSZrNhTxFQ5Pdz2rRpk1//nvz5Pm3atMmS74ihnlPnDOmbNm0a9Ptk9TmdPn26m3uwfe/1dU4rVqxg4sSJHu+u5xQs33t9ndPll1+Or/G6quhjH/sYDoeDRx55hJdeeok333yTa6+9lmeffRaABx98kD179vDhhx96/eKPPPIIjz/+eL/75Ofn89prr/Hiiy92e3MAUlNTefTRR/nKV77S5/Pr6uooLy/nwoUL/OxnP+P8+fNs376d8PDwXvfvrcclPT2dyspKT0Z0sEW0wfyfvDqnUXZOho7j14vQas+g3/AM7tl3yj8nRuD7pM5JnVOAzqmlpcX3lbmGlyQlJRkHDhwwDMMwGhoaDE3TjN27d3sez8/PN+Li4rw9nGEYhlFeXm7k5+f3e2trazN+//vfG/Hx8d2e63Q6Dbvdbrz22mtev15bW5sRGRlprF271uvn1NXVGYBRWVnp9XOCgfb2duPNN9802tvbrVYZNFLdpXobhg/dT75rGD+INYz/mWAYbY2+kesH1eaBR6q3Ych1l+ptGIZRWVlpAEZdXZ3Pjun1UFF1dTVpaWmAOQwRFRVFQkKC5/GEhIRu3UzekJKSQkpKyoD7LVmyhNraWvbs2cOCBQsA2Lx5M7qus3jxYq9fz+gYv+/aozKSkVg614lUd6ne4CN3T1LuHQFLyh31bW4BUr1BrrtUb38wqHLorklCvf3uL6ZPn851113HF7/4RfLy8ti+fTsPPPAAd955p6ei6Pz5857xO4BTp07x2GOPsWfPHoqKitixYwe33XYbERERrFmzJiDeCsWooqEMjr1l3vfj3C0KhWJ0M6iqonvuuYewsDAAWltb+fKXv0xUlPlflb97MV5++WUeeOABrrrqKmw2G7fccgtPPfWU53Gn00lBQQHNzc0AhIeH8/777/Pkk09SU1PDmDFjuPLKK9mxY0ePRF+FQuED9v8JdBdMWARjZlpto1AoRiheJ+d+7nPe/Qf1wgsvDEso2Oicrri2tpa4uDirdbzG6KhsiYmJCVjPmK+Q6i7VG3zgruvw1DyoPQs3/Qbmfcrnjr0xqtvcIqR6g1x3qd5gFsjEx8f7NDnX6x6XkRaQjAYCNauxP5DqLtUbhul+aosZtITFwYybfObkDaO2zS1EqjfIdZfq7Q/ErFVkNdISo1wuF+vXrxfnDXLdpXqDD9z3dPxjM/dOCI30ndgAjOo2twip3iDXXao3BOFaRQqFQkFDKRzrmIxKJeUqFAo/owIXhUIxPPa9BIYb0i+D1OlW2ygUihGOClwUCsXQ0d2w54/mfdXbolAoAoDXVUWjFclVRS6XC4fDIS4LXaq7VG8YhvuJf8HLt0B4PDx8DEICm0A4KtvcYqR6g1x3qd7gn6oi1eMygmlpabFaYchIdZfqDUN09yTl3hXwoKWTUdfmQYBUb5DrLtXbH6jAxUukZXO7XC62bNkizhvkukv1hiG611+AgrfN+xYNE426Ng8CpHqDXHep3qCqihQKRTDRmZSbsRRSpllto1AoRgkqcFEoFINHd8OeF837KilXoVAEEBW4jGAcjkEtRRVUSHWX6g2DdD/5LtSfg4gEmH6D/6S8YNS0eRAh1Rvkukv19geqqmgAOquKfJkRrVCI55W7oGA9XHY/XPc/VtsoFIogxR/XUNXj4iW6rlutMCh0Xae8vFycN8h1l+oNg3SvOw/HN5j3F9zjV6+BGDVtHkRI9Qa57lK9wT/XThW4eInb7bZaYVC43W5yc3PFeYNcd6neMEj3fS+BoUPmFZAy1f9y/TBq2jyIkOoNct2leoN/rp0qcFEoFN6ju2GvmilXoVBYhwpcFAqF95zYBPXnISIRpn/CahuFQjEKUYGLl0ibZlnTNGJiYsR5g1x3qd4wCPfOmXLnfQocYf4XG4BR0eZBhlRvkOsu1Rv8c+1UVUUDoKqKFIoO6s7Bk7PN/JYH9kDyZKuNFApFkKOqiixEWja3ruucPXtWnDfIdZfqDV667/2jGbRkLQuaoGXEt3kQItUb5LpL9QZVVWQp0rK53W43+/fvF+cNct2leoMX7m4X7H3JvG9xCXRXRnSbBylSvUGuu1RvUFVFCoXCKk5shIYSiExSSbkKhcJSVOCiUCgGxpOUe3dQJOUqFIrRiwpcvERaNremaaSkpIjzBrnuUr1hAPfaIrMMGoJqmAhGcJsHMVK9Qa67VG9QVUWWoKqKFKOezT+GbT+B7OXw2X9abaNQKAShqoosRFpSlNvt5tixY3K8dR3KjsCHv0V/bjUX/vxNOe4diGvzLvTp7naZU/xD0PW2wAht8yBHqjfIdZfqDSo511KklaHpuk5BQUHwerudcG43bP8lrL0TfpINv1kKG/4D27mdjMl/Ab2h3GrLQRH0bd4Pfbof3wANFyAqBXI+bo1cP4zINg9ypHqDXHep3uCfa6fD50dUKHqjvRnO74azO8zbuV3gbO6+T0gUpF+KUXYUW1M5euG7sODT1vgqTLol5YZa66JQKBSowEXhL1pqoXgnnN0OZ3OhZB/ozu77RCRAxhLIXAoZS2HsHLCHoP/rR9g/+Bm2grdU4GIlNWfh5Lvm/QWftdZFoVAoOlCBi5fYbLJG1Ww2GxkZGYHzbiiDoo7elLO5UHYYuCjvO2YcZHYJVFJyoDe/6TfABz9DO7UZ2psgNCogpzBcAt7mPqRX971/BAyYuBISJ1rm1h8jrs0FINUb5LpL9Qb/XDtVVdEAqKqiXjAMqDkDRbkf9ahUF/bcL3FSR6ByudmzkpAF3pTGGQb8ci7UnoXbX4IZN/j6DBQD4XbCEzOhsQxuexFm3mS1kUKhEIg/rqGqx8VLpGVzu91uDh48yJw5c7Db7cM7mK5DxbHuPSoNJRftpMGYWWZvSuYSs0clZszQ3HWdqqTFpNaehWNviglcfNrmAaaHe8HbZtASlQo511ut1ycjqs2FINUb5LpL9Qb/XDtV4OIl0rK5dV2nqKiIWbNmDf6D7nbChYNmb0pRrnlrqem+jy0Exs3vCFSWQvpiiIj3mXuBbSqpAAUbwNUuIjF0WG1uMT3cO5Ny538a7CHWyvXDiGpzIUj1BrnuUr1BVRUp/IWzxSxNPrvD7FUp3gXOpu77hETChEvNYZ/MJTB+IYRG+k2pOmoyRlQqWlM5nHkfJl/lt9dSXET1aSjcDGgqKVehUAQdKnAZjbTWQdHOj3pUzu/tWfETHv9RxU/mUhg7N7D/eWs29KnXYd/3R3O4SAUugWPvH82fk1aZeUkKhUIRRKjAxUukZXPbbDamTZtmejeWfzR/StEOKO2t4mds90AlZXrvFT8BdMcWAfv+CMfegjU/t8zHW7q1uTA87oYb9v3J3BiEM+VezIhoc2HuUr1BrrtUb1BVRZYgtqqoogA2/RdUnuij4meimUDbmUybkO1dxU8gcbXDTydDWx3cuxEyFlttNPI58jqs+yxEj4FvHAnq/BaFQhH8qLWKLMTlclmtMCiMv3zWnK69uhBPxc+i++DWF+DhAvjaPrjpVzD/bjOICaKgxeVysWPHDlzYYOpqc+OxN6yV8gKPt7DPCnzkru9+3tww/99EBC0joc2luUv1BrnuUr3BP9dONVTkJaI6ps7molXk49YcGDf9H46pV5mz1ArBMAwqKirMNp/+CTi0DvLfgGt+FFQB1sV08xaGYRg0nTuM7fR7SErKld7mEt2leoNcd6ne4J9rp+pxGYl88AsAihOvwJhxo6igpQeTrwZHuDnhXdkRq21GNFmVW807k6+G+AxLXRQKhaIvVOAy0rhwEE5sxNBsnBwTvBOHeU1olFndAmZ1kcI/uNtJr37fvC8gKVehUIxeVODiJWIm/fngCfPnjJuYsvhaOd5dsNvtzJs37yP3nI+bP/ODO8+lh7cg7Cc2EO6qx4gZC1Ovs1rHa0S3uVB3qd4g112qN/jn2qlyXLxERBlaVSEcfR0AbdnDZKZlWuszRGw2G5mZXdynfQw0u7lwY/VpSMy2Tq4fenhLwTCwffgrALT5/wZ2OV8LYtscue5SvUGuu1Rv8M+1U8DVODgQkc29/UkwdJhyLa7kHDZv3izD+yJcLld398hEyLrcvB/Ew0U9vKVQ9CGc34OBhmvmbVbbDAqxbY5cd6neINddqjf459qpAhcvCfps7voS2P+KeX/ZNzEMg4aGhuD37oVe3XM+Yf4M4uEisW1e+C4AFTEzMYTNlCu2zZHrLtUb5LpL9QZVVaTojx3PmNP2Z14OGZdZbeN7OlcoLs6DhjJrXUYShgGHXwOgOPHyoC43VygUClCBy8iguRr2/MG8f8U3LVXxG3HjYfwCwICCt6y2GTmc3wvVhRghkVyIW2C1jUKhUAyICly8JKizuXf+n7mac9ocz2KEdrudJUuWBLd3H/Tp7qkuCs48F5FtfvBV82fO9Sy6YqUsd4S2eQdS3aV6g1x3qd7gn2unCly8JGiritoaYOdvzfvLvunp6rfZbKSmpgavdz/06T79BvPn6fegpTbgXgMhrs3dTjj8NwC0OXfKcu9AXJt3Qaq7VG+Q6y7VG1RVkaU4nU6rFXpnzx+gtRaSJn90Ycf0feutt4LXux/6dE+eDCk5oLvgxEZr5PpBXJsXbobmKohKxZlxuSz3DsS1eRekukv1BrnuUr3BP9dOFbhIxtVmJuUCXP4Q2Lp3yUksneukT/cgn4xOVJsf/LP5c/atYHPIcu+CVG+Q6y7VG+S6S/X2Bypwkcz+tdBYCrHjYc4dVtsEhukdZdEn/wXOFmtdJNNaD8c6kpzn3G6ti0KhUAwCFbhIxe0yJ5wDWPogOEIt1QkYY+dCXAY4m82hDsXQyH8DXK2QPBXGzrPaRqFQKLxGBS5e4nAE2TToR183V0yOSIRLPtPjYYfDwcqVK4PP2wv6dde0j+Z0CbLqIlFt3jlMNOcO0DRZ7l2Q6g1y3aV6g1x3qd7gn2unClwkYhjw/i/M+5d91VxBuRciIiICKOVb+nWf3pHnUrDerIwJIkS0eX0JnN5m3p/90RT/Itx7Qao3yHWX6g1y3aV6+wMVuHhJUCVGHX8Hyo9AaDQs+kKvu7hcLtavXx9c3l4yoHvGEohMNqupzm4PqFt/iGnzQ+sAAzKWQoK5cJsY94uQ6g1y3aV6g1x3qd6g1ipSQEdvy8/N+wvvhYgEa32swGY3V4yGoBsuEsHBv5g/VVKuQqEQiApcpHF2O5zLA3sYLLnfahvr6KwuOvYm6Lq1LpIoPQxlh8EeCjNvstpGoVAoBo0KXKTRmdsy/26ISbPWxUqyl0NoDDRcgJK9VtvIoTMpd+q1o7O3TqFQiEczJK6THUDq6+uJi4ujtraWuLg4a2VK9sPvloNmhwf3QGJ2n7sahoHL5cLhcKAJW/HXa/d1n4Mjr5mT713zaMD8+iLo21x3wxOzoKEE7vjTR71WCHDvA6neINddqjfIdZfqDVBXV0d8fDx1dXXExsb65Jiqx0USH3T0tsy6pd+gpZOWFrkTtHnlPr3LLLpBEn8HdZuf+cAMWsLjYcrqHg8HtXs/SPUGue5SvUGuu1Rvf6ACFy+xPJu78gQc/ad5/4pvDLi7y+Viy5Yt1nsPAa/dp6w2czWqC6HiWGDk+iHo27wzKXfmzeAI6/ZQ0Lv3gVRvkOsu1Rvkukv1hlFeVVRdXc3dd99NbGws8fHxfP7zn6exsdGr5xqGwcc+9jE0TeP111/3r6i/+OBJwIBpa2DMDKttgoOwGJi40ryvqov6p70Zjv7DvD9alodQKBQjEjGBy913382RI0fYtGkTb775Jtu2beO+++7z6rlPPvmkuHHBbtQWw8FXzftXfNNal2Cjc7joWHAuuhg0HH8b2hsgPgPSF1tto1AoFENGROCSn5/Phg0beO6551i8eDFXXHEFTz/9NK+++iolJSX9Pnf//v38/Oc/5/nnnw+QrR/IfQZ0F2Qtg/RLvX6axOmhO/Hafdoa0Gxw4QDUnPWvlBcEbZt75m65A2y9/9kHrfsASPUGue5SvUGuu1RvfyCiquj555/n4YcfpqamxrPN5XIRHh7OunXruPnmm3t9XnNzMwsXLuSxxx7jxhtvRNM0/v73v3PTTTf1+VptbW20tbV5fq+vryc9PZ3KykpPRrTNZsNut+N2u9G7zCHSud3lctG1We12Ozabrc/tTmf3aes7P6AulwuaKnE8Mx/N1YLx6b/DpJU9xgxDQkLQdR232+3ZpnWsP9PX9r7cA3JOXmwfzDk5XroRrWg7+uof4770SyPinHz6PrVUY/x8GpruwvmlXEieIv+cRuL7pM5JndMIPKeWlhbi4uJ8WlUkIoQrLS0lNTW12zaHw0FiYiKlpaV9Pu8b3/gGS5cu5cYbb/T6tR577DEefbRnae3GjRuJjIwEICMjg/nz53Pw4EGKioo8+0ybNo2cnBzy8vKoqKjwbJ83bx6ZmZls27aNhoYGz/YlS5aQmprKxo0bu73ZK1euJCIigvXr15NT8lemuVqoicwmOuMKWhoa2LJlS7d2uP7666msrCQ3N9ezPTo6mtmzZ9PU1MTBgwc921NSUli6dCknTpygoKDAsz2Q59SVNWvW0NLS0uOcLr30UnRdZ+fOnZ7tMTExrFq1iuLiYvbv3+/ZPidiFtlsp3XfX9lUkW75OV133XW0tbV59T71dU4+fZ9K30HTXdREZrMt7wRwos9zWrFiBZGRkV6/T5adkx8/e+qcRu45HT9+nOPHj4s7pxUrVnDkyBFOnTrV45yC/X26/PLL8TWW9rg88sgjPP744/3uk5+fz2uvvcaLL77Y7c0BSE1N5dFHH+UrX/lKj+f985//5OGHH2bfvn1ER0cDDKvH5cKFCyQlJQEBjGibanA8PRetrR7XLX/APsv09iZKd7lcbNy4keuuuw5bl6EBCVG60+lk06ZNfbr3+M+j/jyOp+dioOF66ChEpVhyTp3eH/vYxwgJCQme/6Z+fw2c3437mh+jL/pSr+c0kHuw/ofY3t7Opk2buOaaawgPDxf1X29v7hL+k29tbeWdd97hmmuuISQkRFTvRF/uwfC91985GYbB+vXrPd5dzymYv8vBvIYmJyePnB6Xhx9+mHvuuafffSZOnEhaWhrl5eXdtrtcLqqrq0lL63322M2bN1NYWEh8fHy37bfccgvLli1j69atvT4vLCyMsLCwHttDQkI8H5hO7HY7dru9x759jUX2tf3i43q27/8jtNVD8lQcM2+EjgTj3va32WzdLvJdt/e2f1/ufj+nQWzvy73HuSZlwdh5aBf2E1K4CRZ8ttv+gT4nTdPQNG3Q71Nv24f9PlUVwvndoNmxz70d+0VOg3HvbX9LzqnL9s4v2s6LUF+Og90eiHPqzd3ff0++OqfO1+j6OlZ9Rwz2nDqP1fV4wfa9d7F7Z/AwmOtQMJ2Tr7E0cElJSSElJWXA/ZYsWUJtbS179uxhwYIFgBmY6LrO4sW9V0g88sgjfOEL3VdOnj17Nk888QSf+MQnen1OUOFsgdxfmfev+EafCZWKDqZ/HC7sN9cuuihwGdV0TvE/aRVEp/a/r0KhUAhAxNVw+vTpXHfddXzxi18kLy+P7du388ADD3DnnXcybtw4AM6fP+8ZvwNIS0tj1qxZ3W5gjv9lZw886+zFBLycev/L0FQOcekw+7ZBP13TNGJiYkSWgQ/JPacjGD21FVrr/eI1EEHX5obxUeAywNwtQefuJVK9Qa67VG+Q6y7VG/xz7RRRVQTmBHQPPPAAb7zxBjabjVtuuYWnnnrKk79y5swZsrOz2bJlCytWrOj1GN7kuFxM51pFvhyfGxC3C56eD7VF8LGfwmLv5qsZ1RgGPHMpVJ2AW34Ps2+12sh6ivPg99dASBR86wSERlltpFAoRhn+uIaK6HEBSExMZO3atTQ0NFBXV8fzzz/vCVoAsrKyMAyjz6AFzBl0BxO0dKVrkpPfOfw3M2iJTIb5nx7SIXRd5+zZs4H19hFDcte0LpPRWTOLbtC1eWdvy4wbBgxags7dS6R6g1x3qd4g112qN/jn2ikmcLGarlnefkXX4YMnzPuXfQVCI4d0GLfbzf79+wPn7UOG7N45XHRiEzhbfS82AEHV5q52MwAGmHP7gLsHlfsgkOoNct2leoNcd6ne4J9rpwpcgo3jb0NFPoTFwqVfGHh/xUeMmw+x46G90cx1Gc2c/Be01EB0GmQvt9pGoVAofIYKXIIJw4D3f27ev/TzEBFvqY44bDbIud68P9rXLuocJpp9K9h6lkQqFAqFVFTg4iUByeY+vQ3O7wFHOFz21WEdStM0UlJSxGahD9k9pzPPZb2Z5BxAgqbNW+ug4G3zvpcrQQeN+yCR6g1y3aV6g1x3qd4wyquKrCKgVUV/vNEc4rj0i3D9z/z7WiMVtwt+NtkcJvnsm5C9zGqjwLP3j/DPByFlOnw11zNxoUKhUASaUV1VZDV+T4o6v8cMWjQ7LH1w2Idzu90cO3ZMbDLXkN3tDnPFaAh4dVHQtLlnJejbvQ5agsZ9kEj1BrnuUr1BrrtUb1DJuZbi9zK0939h/pxzOyRkDvtwuq5TUFAgtnxuWO6dw0X5b5p5QwEiKNq8thjOvG/e96KaqJOgcB8CUr1BrrtUb5DrLtUbVDn0yKX82Ee9A5c/ZKnKiGDSSnPStfpzULLPapvAcmid+TNrGcRNsNZFoVAo/IAKXIKB7U+aP3M+Dqk5lqqMCEIiYMrV5n2LJqOzhG5T/Hvf26JQKBSSUIGLl/S20qhPqC366L/kZd/02WFtNhsZGRn+8/YjPnHvnIwuP3CBi+VtXnoIKo6BPQym3zCop1ruPkSkeoNcd6neINddqjf459qpqooGwO9VReu/BXm/g4kr4DP/8P3xRyutdfCTSaA74f5dkDLVaiP/8873IPcZmHET3P6i1TYKhUKhqoqsxC/Z3I3lZukqwBW+620B03ffvn1is9CH7R4eBxM7ZowN0GR0lra57oZDfzXvezl3S1ekfl6keoNcd6neINddqjeoqiJL8Us294e/AVcrjF8A2Vf69NC6rlNUVCQ2C90n7l2riwKApW1++j1oLIWIBJh89aCfLvXzItUb5LpL9Qa57lK9QVUVjSxa62DXc+b9ZQ+rScL8Qc71gAYle6HunNU2/qVz7paZnwRHqLUuCoVC4UdU4GIVu56DtnpIyYGpH7PaZmQSnQoZl5n3j71lrYs/aW+C/I7hsLl3WuuiUCgUfkYFLl7i08zo9mbI/bV5/4pvmosD+hibzca0adPEZqH7zN0zXOT/PBfL2vzYenNF7IQsmHDpkA4h9fMi1Rvkukv1BrnuUr1BVRVZgl+qinb+Dt7+FsRnwIP7zGnqFf6h5gz8ci5oNvj3kxCVZLWR7/nTrXByEyz/D1j5XattFAqFwoOqKrIQl8tHKw27nbDjKfP+0q/5LWhxuVzs2LHDd94BxKfuCVmQNhsMHY6/Pfzj9YMlbd5YDoWbzftDqCbqROrnRao3yHWX6g1y3aV6gw+vnV1QgYuX+Kxj6tA6qCuGqFSY/2nfHLMXDMOgoqLCd94BxOfuAZqMzpI2P/w3MNwwfiEkTRryYaR+XqR6g1x3qd4g112qN/jw2tkFFbgEEl2HD54w7y/5qjk1vcL/TO/IcyncDG0N1rr4Gs8U/0PvbVEoFApJqMAlkBx7EyqPQ1gcLPy81Tajh9QZkDgR3G1w8l9W2/iOiuPmIpI2B8z6pNU2CoVCERBU4OIldrt9eAcwDPjgF+b9RV+EcD8sH9AFu93OvHnzhu9tAT5317SATEYX8DY/1DF3y+SrISp5WIeS+nmR6g1y3aV6g1x3qd7gg2tnL6iqogHwWUZ04RZ46SZwRMA3Dg/7QqMYJMV58PtrIDQGvl0IjjCrjYaHrsNTc81FOm99HmbdYrWRQqFQ9EBVFVnIsDOj3/+5+XPBZwMStLhcLjZv3iw2C93n7uMXQnQatDfA6W2+O24XAtrmxTvNoCU0xicTGEr9vEj1BrnuUr1BrrtUb1BVRZYyrI6p4l1w5n0zF2HJA76T6gfDMGhoaBCbhe5zd5utYwkA/DYZXUDbvDMpd8YNEBo57MNJ/bxI9Qa57lK9Qa67VG9QVUVy6cxtmXMnxKdb6zKa6awuKlhvrqYsFVcbHPm7eV9VEykUilGGClz8TdlR80KJBlc8ZLXN6CZrGYTHQVOFOdQilRMbobUWYsZB1hVW2ygUCkVAUYGLlww5M7pz3pYZN0DyFN8JDYDdbmfJkiVis9D94m4P+SgfxA/VRQFr885hotm3gs03ryX18yLVG+S6S/UGue5SvcE/VUUqcPGSIS0UVXPGnNkUzMUUA4jNZiM1NVXsolx+c+8cLjr2hlmi7kMC0uYtNXD8HfO+D4eJpH5epHqDXHep3iDXXao3+GeRRXmtYBFOp3PwT9r+lDkd+6RVMG6ez536w+l08tZbbw3N22L86j7pKrMkvbYISg/69NABafMjr4O7HcbMgrRZPjus1M+LVG+Q6y7VG+S6S/WGIV47B0AFLv6ioQz2/cm8v+xhSxQkls514jf30EiYfJV53w/DRX5v84Mdk87Nud3nh5b6eZHqDXLdpXqDXHep3v5ABS7+4sNfmVPMT1gEmZdbbaPoyvSORReP+XfRRZ9TcxaKdgAazLrVahuFQqGwBBW4+IOWGtj1e/P+sofNKecVwcPUa805dcqPQlWh1Tbec2id+TP7Sogbb62LQqFQWIQKXLzE4XB4v3Pec9DeCKkzzYukBTgcDlauXDk47yDB7+4RCWZpNPh0Mjq/ehuGX1eClvp5keoNct2leoNcd6neMMhrp5eowMXXtDfBzt+Y96/4hqW9LREREZa99nDxu7unusi3w0V+876w31xZ3BH+0VCXj5H6eZHqDXLdpXqDXHep3v5ABS5e4nVi1N4/QnMVJGTBzJv96tQfLpeL9evXi0zoCoj7tI7p/8/tgvoSnxzSr96dSbnT1vhlZXGpnxep3iDXXao3yHWX6g1qraLgx9UOO54271/+dbDL69YbNcSONROnAY69Za3LQLhdcOiv5v25d1rrolAoFBajAhdfcvDPUH/eXIV47qestlEMhJ+Gi3zOqa3QVA6RSeacQAqFQjGKUYGLr9DdsP1J8/6S+yEk3FIdhRfkdAQup9+H5mprXfqjMyl31i3msgUKhUIxitEMietkB5D6+nri4uKora0lLi6u7x2P/B3W3QPh8fCNwxAWEyjFXjEMA5fLhcPhQBNWjh1Q918vhfIjcNNvYd5dwzqUX7zbGuFnU8DZDF94FyYs9M1xL0Lq50WqN8h1l+oNct2legPU1dURHx9PXV0dsbG+yc9TPS6+wDDg/V+Y9xd/yfKgpZOWlharFYZMwNx9PFzkc+9jb5pBS+IkGL/At8e+CKmfF6neINddqjfIdZfq7Q9U4OIl/WZGF75rrnsTEgmLvxw4qX5wuVxs2bJFbBZ6wNw7h4tOvmuWsg8Dv3h3nbvFj/9pSf28SPUGue5SvUGuu1RvUFVFwUtnb8uCz0FkorUuisGRNhviM8HVYgYvwURDqZmYCzDnNktVFAqFIlhQgctwKfoQzm4HW4iZlKuQhaYF79pFh/8Ghm6WbSdOtNpGoVAoggIVuAyXzt6WeXcF3foxEqeH7iSg7p3DRcc3mHPxDAOfeh941fw51/dT/PeG1M+LVG+Q6y7VG+S6S/X2B6qqaAA6q4p6zYguPQy/vRw0GzywG5ImWSOpGB66G34+DZoq4NOvweSrrDaC8nz49WXmYpD/fkINQSoUCpH0ew0dIqrHxUt0Xe+58YMnzJ8zbgy6oEXXdcrLy3v3DnIC7m6zQ07HEgDDGC7yqXfnFP9TVgckaJH6eZHqDXLdpXqDXHep3tDHtXOYqMDFS9xud/cNVYVw5DXz/hXfDLzQALjdbnJzc3t6C8AS95zOPJe3YIh/aD7z1nU4tM6874eVoHtD6udFqjfIdZfqDXLdpXpDL9dOH6ACl6Gy4ykzcXLyNTB2jtU2iuGSfSWExUJjmbnwopUU7YC6YtNn6nXWuigUCkWQoQKXoVB/AfavNe8ve9haF4VvcITC1GvN+8fesNalc+6WGTeqpSMUCoXiIlTg4iXdplnOfQbc7ZCxBDKXWCfVD5qmERMTI256aLDQvbO6KP9NczbkQeITb2crHPmHeT9Aw0Qg9/Mi1Rvkukv1BrnuUr0BvzirqqIB6JER3VwNT8wCZxPc/VeYco3Vigpf0dYIP5kI7jb48nZImxV4hyOvw7rPQuwEeOgQ2NT/FgqFQi6qqshCPJnReb8zg5a02TD5amul+kHXdc6ePSs2C90S97Doj0qhh1Bd5BPvzmqiObcFNGiR+nmR6g1y3aV6g1x3qd6gqoosxe12m/+R7/ytueGKb/h17Zjh4na72b9/v9gsdMvcuw4XDZJhezdXw4mN5v0ADhOB3M+LVG+Q6y7VG+S6S/UGVVVkPXv+AC015vTrM26y2kbhD6Z9DDQ7lB2C6tOBfe0jfwfdafbmpU4P7GsrFAqFEFTg4i2uNjMpF+Dyh8xJyxQjj8hEyLrcvB/otYs8K0HfGdjXVSgUCkGowMVLbEf+Dg0XIGYczA3+C4umaaSkpIjNQrfUvXMyukEOFw3Lu/o0FO80l4+Ydcvgnz9MLG/zISLVG+S6S/UGue5SvUFVFVmCJyP68dnENv//9s48Pqar/+OfSSYz2RPZJNHIWpGoilpDSWKLRjUtitpib6tVtbQIHgTlUS2quqCSKJoqoUqKFKmd1hpJpAQPKkkJWcg2y/n9Mb9MM7LNTGY7fN+v17zIueee+z537r3nO/eec8//gMhPaBbop52iv4GVwQAEwPRswK6p/rf5+3Lg8BLALwIYtUv/2yMIgjAANKrImDy8CVg1AV6KMbaJWshkMly5coXbzlxGdXdoBjRrB4AB2XvVXk1rb8aqPSYybKfcKoy+z7WEV2+AX3devQF+3Xn1BqhzrvHp9K5iyCwHyOVyZGdnczt8zujuWowu0tr773NAwTXAwhoI6q/ZujrCJPa5FvDqDfDrzqs3wK87r94ADYc2KszCGug4wdgahKGoCiBuHAHKCvW7raq7LS37cRMYEwRBGAtuApcHDx5g+PDhsLe3h6OjI8aNG4dHjx7Vu054eDgEAoHK55133tFq+/I2wxUjTohnA5fnAZdAxfDkqner6AOZBLi8Q/F/Iz0mIgiC4AluApfhw4cjIyMDqamp2LNnD44cOYKJEyc2uN6ECROQm5ur/Cxfvlw7gQ7jtVvPSJiZmaF58+Yw4/CV8SbjXnXXJUu9SRe18s45BJTeB2xcFR1zjYTJ7HMN4dUb4NedV2+AX3devQHoxZmLUUVZWVkIDg7GH3/8gfbt2wMA9u3bh6ioKNy5cweenp61rhceHo6QkBCsWrVK623ro0c0wQl3zwPrwhV9Tz6+DlhY6X4b28cq7rh0ehd4ZZnuyycIgjAi+mhDhTopRc+cPHkSjo6OyqAFAHr16gUzMzOcPn0ab7zxRp3rbtmyBZs3b4a7uzv69++PefPmwdraus78FRUVqKioUP5dXFwMACgvL4eVlaLhMjMzg7m5OWQymUrHo6p0qVSK6vGgubk5zMzM6kyXSCQqDkKh4muRSqVqpVtYWEAul6v03pbL5cjMzMQLL6hOFCgQCCAUCut0N4U6yWQyZGVl1en+ZF31VieXVhDaPwdB8R0g5xAk/n3qdZfJZMjIyECbNm2U5VenxvdUUQLhlb0QAJC3fhOyavvM0N9TQ+6aHHuG/J6kUikyMjLQqlUriEQivZ1P+qhTbe6GvEZoW6fKykqkp6ejVatWSj9Tue41VKe63E3huldfnQQCAS5evIjg4GCYm5ur1MmUr+WAfkYVcRG45OXlwc3NTSVNKBTCyckJeXl5da43bNgweHt7w9PTE5cuXcLMmTORnZ2N5OTkOtdZunQpFi5cWCP94MGDyoCnefPmaNu2LS5duoRbt24p8wQGBqJly5Y4c+YM7t27p0wPCQmBt7c3jhw5gpKSEmV6aGgo3NzccODAAZUvOyIiAlZWVkhJSVFxiIqKQllZGQ4fPqyyH/r164f79+/j5MmTynRbW1s8evQIDg4OSE9PV6a7urqiS5cuuHr1KrKzs5XpplYnqVSKpk2b4o8//lCm29nZoUePHrh9+zYuXLhgkDq9IA6GP+4AWXtwIAdq1SkoKAhSqbTB78mr4ChekpYDzs/jttQZF6qVY6zvydfXF3Z2do069ozxPd25c0ev55M+63Tnzh2jXCO0rVN6ejru3LmDO3fuaPw9mUKdqrub2nWvrjp169YNt2/fxu3bt9X+nkylTp07d4auMeqjolmzZuG///1vvXmysrKQnJyMxMRElS8HANzc3LBw4UK8++67am3v0KFD6NmzJ65duwZ/f/9a89R2x8XLywu5ublwdnYGYHoRbW1RulQqxYEDB9C3b1+VZ4w8ROkSiQSpqal1uhvsjgsAwf+OQ7g5GrBqAsmUTMDcok73Ku9XXnkFFhYWDX5P5lsGwOzmEaDHXMhfnm6wOlVPr/qeGnI39t2JuupUWVmJ1NRU9O7dG5aWliZ3d6K+OtXmbkp3J+pyLy8vx/79+9G7d29YWFiY1HWvoTrV5W4K17366sQYQ0pKitK7ep1M+VoOKNpQFxeXp+dR0fTp0zF69Oh68/j5+cHd3R3//POPSrpUKsWDBw/g7u6u9vY6deoEAPUGLmKxGGKxuEa6hYWF8oCpwtzcXHnbrjpVX6C66U+Wq026mZlZrZ2gzMzMas1fl7up1UmTuuqlTn7dAGtnoLQAFnfPAH7hDbpX3dqt1734LnDzqCKx9Zsa1Ukmk9W4mNSWVpVeG0+my2QyCIVCVFRU1PreBXXLqeLJi1d96fW5N1SnKm+ZTIbKykqNHI1dJ326W1hY1Dhu6rtGaHo+VW2j+jFuate9uupUVVb18kztuveke9Uxo0k7ZEp10jVGDVxcXV3h6uraYL7Q0FAUFhbi7NmzaNeuHQDF3RO5XK4MRtSh6tahh4eHxq689eY2MzNDYGAgd96AibmbmQOBUcD57xUvo6slcFFm1cQ7fTsABjQPBZr4qKXCGENeXh4KCwvVyq8JjDF4eXnh9u3bXM2Hwqs3oH93R0dHuLu767xskzo/NYRXd169gWd4VBEAvPLKK8jPz8c333wDiUSCMWPGoH379ti6dSsA4O+//0bPnj2xadMmdOzYETk5Odi6dSuioqLg7OyMS5cuYerUqXjuuefw+++/q71dGlVE4K/9wNbBigk2p2YAujgRv34ZyE8HXl0JtB+r1iq5ubkoLCyEm5sbrK2tuWuoCcPAGENpaSn++ecfODo6avVDjSB0xTM7qghQjA56//330bNnT5iZmWHgwIH44osvlMslEgmys7NRWloKABCJRPjtt9+watUqPH78GF5eXhg4cCDmzp2r1fbruk1sqkilUpw5cwYdO3as8xagqWJy7r5hgMgWKLkL3D0HPNe+1mxqe+dnKIIWcxHQqu4RcdWRyWTKoKWqr5UuYYzh8ePHsLGx4Sog4tUb0K971QjIf/75B25ubrU+MtAWkzs/NYBXd169Af20ndzsAScnJ+Xdldrw8fFR6Vjk5eWl0Z2VhuDkxpQSxhju3bvHnTdggu4WlsDzfYCMZMXL6OoIXNT2rnrF//N9FBN3qkHVM+76hvI3BsaYsnMeTwEAr96A/t2rjhWJRKLTwMXkzk8N4NWdV29AP20nfw/MCMIYBFVNuviLYjZnbZHLgUs/Kf6vxSv+eWucCeNBxwrxtEKBC0GoQ0BvxaOdBznAvSval3PzqOKRk6UD0CJSd34EQRDPCBS4qIkub7UaAnNzc4SEhHDnDZiou6X9v3MJZe2pNYta3pe2Kf5t9QYgrDns3lgIBAJYWVlx9yu9sd4LFixASEiIbqXUhNd9bpLnp5rw6s6rN6CftpMCFzXhbRiamZkZvL29ufMGTNi96nHRldonXWzQW1IGZP6s+L+JzQQtEAggFov10oiOHj26xiztAoEA165dAwBcvHgRr732Gtzc3GBpaQkfHx8MGTJE+e6mmzdvqqzn5OSEsLAwHD16VMW7tLQUs2fPhr+/PywtLeHq6oqwsDD8/PPPOq9TQ6hTJzMzM1hbW+Pu3bsq6+bm5kIoFEIgEODmzZvK8t566y14eXnBysoKQUFBWL16taGrBcCEz0814NWdV29AP20nf3vBSPA4qujQoUPceQMm7B4YBQjMgNyLQOGtGosb9M7+FagsARyaA166fw12Y2CMobi4WG+d//r27asyS3tubi58fX1x79499OzZE05OTti/fz+ysrIQHx8PT09PPH78WKWM3377Dbm5uThy5Ag8PT3x6quvIi8vT+n9zjvvIDk5GWvWrMGVK1ewb98+DBo0CAUFBXqpU11oUidPT08kJiaqpCUmJqJZs2YqaWfPnoWbmxs2b96MjIwMzJkzB7Nnz8aXX36p9/o8icmen2rAqzuv3sAzPqrI2PDWm5sxhpKSEu68ARN2t3FRvDDuf8cVj4tCJ6ksbtC7ajTRi4N18i4YxhjKJLqZwEwul+NxuQRmIolav5CsLMw1ujsjFotrfcv18ePHUVRUhA0bNiiHefr6+iIiIqJGXmdnZ7i7u8Pd3R2xsbFISkrCqVOnEB4eDsYYdu/ejdWrVyMqKgqAYqRh1QsrG+Lbb7/F4sWLUVBQgFdffRXr16+Hg4MDjhw5gp49e+L27dsq/h9++CHOnj2Lo0ePNqpOQ4cORUJCAmJjY5Vp8fHxiImJwaJFi5RpY8eqvuvHz88PJ0+eRHJyMt5//3216qgrTPb8VANe3Xn1BvTTdlLgQhCaENRfEbhcqRm41Mvj+8C13xT/f3GwTlTKJDIE/2e/TsrSlMy4SFiLGn/5cHd3h1Qqxc6dOzFo0CC1gqGysjJs2rQJgOJ9TdXLSklJwYABA2BnZ6e2w7Vr17Bt2zb88ssvKC4uxrhx4zBp0iRs2bIF3bt3h5+fH77//nt89NFHABTDi7ds2YLly5c3uk6vvPIKEhIScOzYMbz88ss4duwYHj58iP79+6sELrVRVFQEJycntetJEE8L9KiIIDShZT/Fv7dOAo/u1Z+3Ohk7AbkU8AgBXAP1ombK7NmzB7a2tsrPm2++CQDo3LkzYmNjMWzYMLi4uOCVV17Bp59+ivz8/BpldOnSBba2trCxscGKFSvQrl079OzZU7l83bp1OHHiBJydndGhQwdMnToVx48fb9CtvLwcmzZtQkhICLp37441a9YgKSlJOfP8uHHjEB8fr8z/yy+/oLy8HIMH1x6AalInCwsLDB8+HBs3bgQAbNy4ESNGjGhwvpcTJ07gxx9/xMSJExusH0E8dTCiXoqKihgA9vDhQ2OraIRMJmP5+flMJpMZW0VjTN79m26Mzbdn7M8EleR6vdf3VKxzYq1WmywrK2OZmZmsrKxMmSaXy9njColOPo/KK1nho1L2qLxSrfxyuVxt95iYGNarVy929epV5efu3bsqee7fv8+2bdvGpk+fzvz8/JijoyO7dOkSY4yxGzduMABs9+7d7K+//mLJycksICCApaenM7lcziorK5U+lZWV7MiRI2zZsmWsd+/eTCAQsLi4uDrd5s+fz3x9fVXSCgsLGQCWlpbGGGMsPz+fWVhYsJMnTzLGGOvfvz8bO3Zsg/VWp05nzpxhFy9eZLa2tiw3N5fZ2tqy9PR0dv78eQaA3bhxo0a56enpzMXFhS1atKje7dd2zOgCkz8/64FXd169GWPs4cOHDAArKirSWZkUuDRAVeCiy51OcM7vyxVByOZB6uW/f02Rf0ETxkrytdqkvhohQxATE8Oio6PVzl9RUcGCg4PZqFGjGGP/NvLnz59X5tm2bRsLCAhg5eXl9Za1aNEiZmFhwSoqKmpdrk7gwhhjAwYMYBMnTmR5eXlMKBSyY8eOqV0fderUvn17Fh4ezjp06MAYY3UGLhkZGczNzY3FxsY2uE2ejxni6UEfbSg9KlKT2qaoN2UkEgn27t3LnTfAgXvL/op/r6cB5cXK5Dq9q97d4h8B2LoZxlFD5HI5CgsLIZfLja0CkUgEf3//GiNwqjNo0CAIhUKsXbu2Xu/g4GBIpVKUl5fXWdatW7dUhiSfOnVKORtvFePHj8ePP/6IdevWwd/fH127dtVJnUpKSiCXyzF27FikpaXV6IRbnYyMDERERCAmJgZLlizRaPu6xOTPz3rg1Z1Xb0A/bScFLk8xPA6dq8Kk3V0DAecAQFYJXD2gsqiGN2PVRhOZ1rtbTIE9e/ZgxIgR2LNnD/766y9kZ2djxYoVSElJQXR0dJ3rCQQCfPDBB/jvf/+rnFg1PDwc3377Lc6ePYubN28iJSUFsbGxiIiIqHdWWktLS8TExODixYs4evQoPvjgAwwePFhlFFFkZCTs7e2xePFijBkzRud1mjBhAu7du4fx48fXuvzy5cuIiIhAnz59MG3aNOTl5SEvLw/37mnQz0qHmPT52QC8uvPqrQ8ocCEITREIFKOLAMXoovq48wfw8AZgYfNvx15CSXBwMKytrTF9+nSEhISgc+fO2LZtGzZs2ICRI0fWu25MTAwkEgnWr18PQBFcJCYmok+fPggKCsLkyZMRGRmJbdu21VtOQEAABgwYgKioKPTp0wcvvvgivvrqK5U8ZmZmGD16NGQyGUaNGqXzOgmFQri4uNQ58+/27dtx7949bN68GR4eHspPhw4d6nUhiKcRGg5NENrQsj9wbCVwNRWQlCtmkK6NqrstQf0BkY3h/EyIhISEOpf5+flh3bp19a7/5MzvVVhbW+P+/fsoLlY8rps9ezZmz56tkduCBQuwYMECAMC7775bb96///4bUVFR8PDwqDefunWSyWRK9ycJCQlRqXN1T4J41qHARU3q+iVkqgiFQkRERHDnDXDi7tkWsPNUTJh4PQ0I7FvTW1oJXE5W/F9H727RFwKBAHZ2dtzNm2MI76KiIqSnp2Pr1q3YvXu3zsrldZ9zcX7WAa/uvHoD+mk76VHRU4yVlZWxFbTG5N3NzGqdu0jFO+cgUPYAsG0K+IYZWFBzeGtAq9C3d3R0NPr06YN33nkHvXv31mnZvO5zkz8/64FXd1699QEFLmrCW8coqVSKlJQU7rwBjtxb/n/gkv0rIJPW9L6YpPj3hUGAuWn/UmJ6nqtIXxjCOy0tDaWlpVi5cqVOy+V1n3NzftYCr+68egP6aTspcCEIbfHuClg1AUoLFG/SrU55kSKgAYA2NJqIIAhCV1DgQhDaYi5UzBgN1BxdlLkbkFUAri0B9xcN70YQBPGUQoELQTSGqsdFV/Yq3tlSRfWZoDntx0AQBGGKCBhvD1gNTHFxMRwcHFBYWAgHBwdj66gNYwxSqRRCoZC7DoBcuUvKgOX+gOQx2IQ0SN1egPBxHgSrWgNgwIeXAUevRm+mvLwcN27cgK+vLywt6xh63QiYYvoPCAQC09/n1eDVG9C/u76OGa7Ozyfg1Z1Xb0AxKs/R0RFFRUX1vghSE+iOy1NMWVmZsRW0hht3Cysg4P9nKM76ReGdvh0AA7xf1knQYih4/Q3DqzfArzs352ct8OrOq7c+oMBFTXjrzS2VSnH48GHuvAEO3YNeU/x75RccPnQIuPT/o4lM/N0t1WGMoaSkhLuGlFdvgF937s7PavDqzqs3QKOKCMI0adEHMLOA4P5f8Cw8DcG9K4C5GAiue64dwvD4+Phg1apVxtYgCKKRUOBCEI3F0gHw7Q4AaHM7UZEW2BewcjSekwlx7949vPvuu2jevDnEYjHc3d0RGRmJ48ePK/P4+PhAIBAgKSmpxvqtWrWCQCBQTh3w4MEDTJ48GYGBgbCxscELL7yAKVOmoKioyFBVUpKWlqbso1L9M3fuXABAaWkpZs+eDX9/f1haWsLV1RVhYWH4+eeflWX06NFDuZ6lpSVatGiBpUuX1rgTs3PnTnTu3BkODg6ws7NDq1at8OGHHxqyugRhEpj2W7GIRsHj66Gr4M49qD+QcxAi2WPF3zQTtJKBAweisrISiYmJ8PPzQ35+Pg4ePIiCggKVfF5eXoiPj8fQoUOVaadOnUJeXh5sbP6d5+nu3bu4e/cuVqxYgZYtWyIrKwszZsxAbm4utm/fbrB6VSc7O1ul46GtrS0A4J133sHp06exZs0aBAcHo6CgACdOnKhR9wkTJiAuLg4VFRU4dOgQJk6cCEdHR+X8SQcPHsSQIUOwZMkSvPbaaxAIBMjMzERqaqrhKlkN7s7PavDqzqu3XmBEvRQVFTEArKioyNgqhClTks/YfAfG5tsztsybMUmFTosvKytjmZmZrKys7N9EuZyxikfG+cjlank/fPiQAWBpaWn15vP29mazZs1iYrGY3bp1S5k+YcIENnnyZObg4MDi4+PrXH/btm1MJBIxiURS7zbi4uLY0KFDmbW1NfP09GRffvmlcvmYMWNYv379VNaprKxkrq6ubMOGDbWWefjwYQaAPXz4sNblDg4OLCEhoU4nxhgLCwtjU6ZMUUl76aWX2BtvvKH8e8qUKSw8PLzecp6k1mOGIAyMPtpQCuHURC6XG1tBI+RyOe7fvw8XFxeYmfH1RJBLd1s3MCd/CB5cA/PqDIFQpP9tSkqBTzz1v53aiL2r1mzXtra2sLW1xa5du9C5c2eIxeI68zZt2hSRkZFITEzE3LlzUVpaih9//BG///47Nm3aVOs67P+HiRYWFsLe3r7BX6WffvopYmNjsXDhQuzfvx9TpkxBixYt0Lt3b4wfPx7du3dHbm6ucgboPXv2oLS0FEOGaHcHzd3dHSkpKRgwYADs7OxqdX8y7dixY7hy5Qqef/55lXK2bt2Ky5cv44UXXtDKRVdweX7+P7y68+oN6Kft5GsPGBGZTGZsBY2QyWQ4efIkd94Av+6yrlPxt2NHSLtMNbaKySAUCpGQkIDExEQ4Ojqia9euiI2NxaVLl2rNP3bsWCQkJIAxhu3bt8Pf3x8hISF1ls8Yw61bt7B48WJMnDixQZ+uXbti1qxZaNGiBSZPnoxBgwYp5yDq0qULAgMD8f333yvzx8fH480331Q++qmL5557Thmk2draKh8FrVu3DidOnICzszM6dOiAqVOnKvv2MMbw+LHi0eJXX30FW1tbiMVidO/eHXK5HB988IGy/MmTJ6NDhw5o3bo1fHx8MHToUGzcuBEVFRUN1lnX8Hp+Avy68+oN6KftpDsuBKEj2ItD8OcdO0Q1e8kwG7SwVtz50AFyuRzFJSWwt7NT7xedhbXaZQ8cOBD9+vXD0aNHcerUKfz6669Yvnw5NmzYgNGjR6vk7devH95++20cOXIEGzduxNixY+stu7i4GEOGDEFQUBAWLFjQoEtoaGiNv6uPNBo/fjzWrVuHjz/+GPn5+fj1119x6NChBss9evSoyh2VJk2aAAC6d++O69ev49SpUzhx4gQOHjyI1atXY+HChZgzZ44y//DhwzFnzhw8fPgQ8+fPR5cuXdClSxflchsbG+zduxc5OTk4fPgwTp06henTp2P16tU4efIkrK3V/z4IgnfojgtB8IpAoHhco6uPhbX6eTV8e6elpSV69+6NefPm4cSJExg9ejTmz59fI59QKMTIkSMxf/58nD59GsOHD6+zzJKSEkRFRcHW1hbJycmwsLDQeBc+yahRo3D9+nWcPHkSmzdvhq+vL7p169bger6+vggICFB+qgd/FhYW6NatG2bOnIkDBw4gLi4OixYtQmVlpTKPg4MDAgIC0KFDB2zbtg1ffvklfvvttxrb8ff3x/jx47FhwwacO3cOmZmZ+PHHHxtdb4LgCQpc1IS31ywLBALY2dlx5w3w686rN6BwNzMzM5h7cHCw8jHJk4wdOxa///47oqOjlXcunqS4uBh9+vSBSCRCUlISrKys1NruqVOnavwdFBSk/NvZ2Rmvv/464uPjkZCQgDFjxqhZI/UJDg6GVCpFRUVFrXe3bG1tMWXKFMyYMaPel9P5+PjA2tq6zv2oL3g/znl059Ub0E/bSY+K1IS3oWhCoRA9evQwtoZW8OrOqzeguLjoah6R6hQUFODNN9/E2LFj8eKLL8LOzg5//vknli9fjujo2l/QFxQUhPv379f5+KMqaCktLcXmzZsBAPn5+QAAV1dXmJub1+lz/PhxLF++HK+//jpSU1Px008/Ye/evSp5xo8fj1dffRUymQwxMTHaVFtJeHg43nrrLbRv3x7Ozs7IzMxEbGwsIiIi6p377O2338aiRYuwY8cODBo0CAsWLEBpaSmioqLg7e2NwsJCfPHFF5BIJOjdu3ejHDWF5+OcV3devQH9tJ18tcZGhMdRRbdv34aXlxeXvdB5dOfVG1B0FK2srIRIJNLpLyRbW1t06tQJK1euRE5ODiQSCby8vDBhwgTExsbWuZ6zs3Ody86dO4fTp08DAAICAlSW3bhxAz4+PnWuO336dPz5559YuHAh7O3t8fnnnyMyMlIlT69eveDh4YFWrVrB07Nxo7aqRknFxsaitLQUnp6eePXVV/Gf//xHuc9rw8nJCaNGjcKCBQswYMAAhIWFYe3atRg1ahTy8/PRpEkTtG3bFgcOHEBgYGCjHDWF5+OcV3devQH9tJ00O3QDVM0Off/+/XovpqaGRCJBSkoKoqKidPLs35Dw6q5Pb33PDi2Xy1FcXAx7e3uuLoz68H706BGaNWuG+Ph4DBgwQCdl1oa+97m+jhlez0+AX3devQHFXVcXFxedzg5Nd1wIgiDw77syPvvsMzg6OuK1114zthJBELVAgQtBEASAW7duwdfXF8899xwSEhK469dGEM8KdGaqCW+9uQUCAVxdXbnzBvh159UbULgLhULu3HXp7ePjU+8oHl3D8z7n+Tjn0Z1Xb0A/bSf1cWmAqj4uunw+RxCaou8+LsTTBx0zhCmgjzaUn154Roa3Vy3LZDJcuXKFO2+AX3dDeOvrdwZjDGVlZQa946ALePUG9O+ur3J5PT8Bft159Qb003ZS4KImPA6Hzs7O5s4b4Nddn95VIwlKS0t1XjagaOQqKiq4CwB49Qb07151rOh6FAqv5yfArzuv3oB+2k7q40IQHGBubg5HR0f8888/AABra2udPjuWy+WorKxEeXk5d8OhefQG9OfOGENpaSn++ecfODo61vtCPoLgEQpcCIIT3N3dAUAZvOiSqscWVlZWXHUA5NUb0L+7o6Oj8pghiKcJClzUhLdfc2ZmZmjevDl33gC/7vr2FggE8PDwgJubGyQSiU7LlslkyM7Ohre3N1e/0Hn1BvTrbmFhobf9wev5CfDrzqs3oJ+2k0YVNQCNKiIIgiAI7aBRRUaEt97cMpkM58+f584b4NedV2+AX3devQF+3Xn1Bvh159UboFFFRoW33txyuRy3bt3izhvg151Xb4Bfd169AX7defUG+HXn1RvQT9tJgQtBEARBENxAnXMboKoLUElJCVezckokEpSWlqK4uJgrb4Bfd169AX7defUG+HXn1Rvg151Xb0DRdgK6fSEiBS4NUFBQAADw9fU1sglBEARB8ElBQQEcHBx0UhYFLg3g5OQEQDFzrK52uiEoLi6Gl5cXbt++zd1oKF7defUG+HXn1Rvg151Xb4Bfd169AaCoqAjNmzdXtqW6gAKXBqgag+7g4MDdAQMA9vb2XHoD/Lrz6g3w686rN8CvO6/eAL/uvHoDun2fC3XOJQiCIAiCGyhwIQiCIAiCGyhwaQCxWIz58+dDLBYbW0UjePUG+HXn1Rvg151Xb4Bfd169AX7defUG9ONOr/wnCIIgCIIb6I4LQRAEQRDcQIELQRAEQRDcQIELQRAEQRDcQIELQRAEQRDcQIELgLVr18LHxweWlpbo1KkTzpw5U2/+n376CS1btoSlpSVat26NlJQUA5mqool3RkYGBg4cCB8fHwgEAqxatcpworWgifv69evRrVs3NGnSBE2aNEGvXr0a/I70hSbeycnJaN++PRwdHWFjY4OQkBB8//33BrRVRdPjvIqkpCQIBAK8/vrr+hWsA028ExISIBAIVD6WlpYGtFVF031eWFiI9957Dx4eHhCLxWjRooVRri+aeIeHh9fY5wKBAP369TOg8b9ous9XrVqFwMBAWFlZwcvLC1OnTkV5ebmBbP9FE2+JRIK4uDj4+/vD0tISbdq0wb59+wxoq+DIkSPo378/PD09IRAIsGvXrgbXSUtLw0svvQSxWIyAgAAkJCRovmH2jJOUlMREIhHbuHEjy8jIYBMmTGCOjo4sPz+/1vzHjx9n5ubmbPny5SwzM5PNnTuXWVhYsPT0dJP2PnPmDJsxYwb74YcfmLu7O1u5cqVBfaujqfuwYcPY2rVr2fnz51lWVhYbPXo0c3BwYHfu3DFp78OHD7Pk5GSWmZnJrl27xlatWsXMzc3Zvn37DOrNmObuVdy4cYM1a9aMdevWjUVHRxtGthqaesfHxzN7e3uWm5ur/OTl5RnYWoGm7hUVFax9+/YsKiqKHTt2jN24cYOlpaWxCxcumLR3QUGByv6+fPkyMzc3Z/Hx8Qb1Zkxz9y1btjCxWMy2bNnCbty4wfbv3888PDzY1KlTTdr7448/Zp6enmzv3r0sJyeHffXVV8zS0pKdO3fOoN4pKSlszpw5LDk5mQFgO3furDf/9evXmbW1NZs2bRrLzMxka9as0eqa+MwHLh07dmTvvfee8m+ZTMY8PT3Z0qVLa80/ePBg1q9fP5W0Tp06sbfffluvnk+iqXd1vL29jRq4NMadMcakUimzs7NjiYmJ+lKslcZ6M8ZY27Zt2dy5c/WhVy/auEulUtalSxe2YcMGFhMTY5TARVPv+Ph45uDgYCC7+tHU/euvv2Z+fn6ssrLSUIq10tjjfOXKlczOzo49evRIX4p1oqn7e++9x3r06KGSNm3aNNa1a1e9ej6Jpt4eHh7syy+/VEkbMGAAGz58uF4960OdwOXjjz9mrVq1UkkbMmQIi4yM1Ghbz/SjosrKSpw9exa9evVSppmZmaFXr144efJkreucPHlSJT8AREZG1plfH2jjbSrowr20tBQSiUSnk3Y1RGO9GWM4ePAgsrOz0b17d32q1kBb97i4OLi5uWHcuHGG0KyBtt6PHj2Ct7c3vLy8EB0djYyMDEPoqqCN++7duxEaGor33nsPTZs2xQsvvIBPPvkEMpnMUNo6OT+/++47DB06FDY2NvrSrBVt3Lt06YKzZ88qH8tcv34dKSkpiIqKMogzoJ13RUVFjUegVlZWOHbsmF5dG4uu2s9nOnC5f/8+ZDIZmjZtqpLetGlT5OXl1bpOXl6eRvn1gTbepoIu3GfOnAlPT88aJ4A+0da7qKgItra2EIlE6NevH9asWYPevXvrW1cFbdyPHTuG7777DuvXrzeEYq1o4x0YGIiNGzfi559/xubNmyGXy9GlSxfcuXPHEMpKtHG/fv06tm/fDplMhpSUFMybNw+fffYZFi9ebAhlAI0/P8+cOYPLly9j/Pjx+lKsE23chw0bhri4OLz88suwsLCAv78/wsPDERsbawhlANp5R0ZG4vPPP8fVq1chl8uRmpqK5ORk5ObmGkJZa+pqP4uLi1FWVqZ2Oc904ELwx7Jly5CUlISdO3catdOlutjZ2eHChQv4448/sGTJEkybNg1paWnG1qqXkpISjBw5EuvXr4eLi4uxdTQiNDQUo0aNQkhICMLCwpCcnAxXV1d8++23xlZrELlcDjc3N6xbtw7t2rXDkCFDMGfOHHzzzTfGVlOb7777Dq1bt0bHjh2NraIWaWlp+OSTT/DVV1/h3LlzSE5Oxt69e7Fo0SJjq9XL6tWr8fzzz6Nly5YQiUR4//33MWbMGJ3OwGzKCI0tYExcXFxgbm6O/Px8lfT8/Hy4u7vXuo67u7tG+fWBNt6mQmPcV6xYgWXLluG3337Diy++qE/NGmjrbWZmhoCAAABASEgIsrKysHTpUoSHh+tTVwVN3XNycnDz5k30799fmSaXywEAQqEQ2dnZ8Pf31680dHOcW1hYoG3btrh27Zo+FOtEG3cPDw9YWFjA3NxcmRYUFIS8vDxUVlZCJBLp1Rlo3D5//PgxkpKSEBcXp0/FOtHGfd68eRg5cqTyDlHr1q3x+PFjTJw4EXPmzDFIIKCNt6urK3bt2oXy8nIUFBTA09MTs2bNgp+fn959G0Nd7ae9vT2srKzULufZCM/qQCQSoV27djh48KAyTS6X4+DBgwgNDa11ndDQUJX8AJCamlpnfn2gjbepoK378uXLsWjRIuzbtw/t27c3hKoKutrncrkcFRUV+lCsE03dW7ZsifT0dFy4cEH5ee211xAREYELFy7Ay8vLJL1rQyaTIT09HR4eHvrSrBVt3Lt27Ypr164pg0QA+Ouvv+Dh4WGQoAVo3D7/6aefUFFRgREjRuhbs1a0cS8tLa0RnFQFjsxA0/g1Zp9bWlqiWbNmkEql2LFjB6Kjo/Wt2yh01n5q1m/46SMpKYmJxWKWkJDAMjMz2cSJE5mjo6NyCOXIkSPZrFmzlPmPHz/OhEIhW7FiBcvKymLz58832nBoTbwrKirY+fPn2fnz55mHhwebMWMGO3/+PLt69apBvbVxX7ZsGROJRGz79u0qwy5LSkpM2vuTTz5hBw4cYDk5OSwzM5OtWLGCCYVCtn79eoN6a+P+JMYaVaSp98KFC9n+/ftZTk4OO3v2LBs6dCiztLRkGRkZJu9+69YtZmdnx95//32WnZ3N9uzZw9zc3NjixYtN2ruKl19+mQ0ZMsSgrk+iqfv8+fOZnZ0d++GHH9j169fZgQMHmL+/Pxs8eLBJe586dYrt2LGD5eTksCNHjrAePXowX19f9vDhQ4N6l5SUKNsVAOzzzz9n58+fZ//73/8YY4zNmjWLjRw5Upm/ajj0Rx99xLKystjatWtpOLS2rFmzhjVv3pyJRCLWsWNHdurUKeWysLAwFhMTo5J/27ZtrEWLFkwkErFWrVqxvXv3GthYgSbeN27cYABqfMLCwgwvzjRz9/b2rtV9/vz5Ju09Z84cFhAQwCwtLVmTJk1YaGgoS0pKMrhzFZoe59UxVuDCmGbeH374oTJv06ZNWVRUlMHfbVEdTff5iRMnWKdOnZhYLGZ+fn5syZIlTCqVGthac+8rV64wAOzAgQMGNq2JJu4SiYQtWLCA+fv7M0tLS+bl5cUmTZpk8ABAU++0tDQWFBTExGIxc3Z2ZiNHjmR///23wZ0PHz5c67W5yjUmJqZGG3P48GEWEhLCRCIR8/Pz0+p9PwLGDHQ/jCAIgiAIopE8031cCIIgCILgCwpcCIIgCILgBgpcCIIgCILgBgpcCIIgCILgBgpcCIIgCILgBgpcCIIgCILgBgpcCIIgCILgBgpcCIIgCILgBgpcCIIwOUaPHo3XX39d+Xd4eDg+/PBDg3ukpaVBIBCgsLDQ4NsmCKJ2KHAhCEItRo8eDYFAAIFAAJFIhICAAMTFxUEqlep928nJyVi0aJFaeQ0dbPj4+Cj3i7W1NVq3bo0NGzYYZNsE8SxCgQtBEGrTt29f5Obm4urVq5g+fToWLFiATz/9tNa8lZWVOtuuk5MT7OzsdFaeromLi0Nubi4uX76MESNGYMKECfj111+NrUUQTyUUuBAEoTZisRju7u7w9vbGu+++i169emH37t0A/n28s2TJEnh6eiIwMBAAcPv2bQwePBiOjo5wcnJCdHQ0bt68qSxTJpNh2rRpcHR0hLOzMz7++GM8OYXak4+KKioqMHPmTHh5eUEsFiMgIADfffcdbt68iYiICABAkyZNIBAIMHr0aACAXC7H0qVL4evrCysrK7Rp0wbbt29X2U5KSgpatGgBKysrREREqHjWh52dHdzd3eHn54eZM2fCyckJqampGuxZgiDUhQIXgiC0xsrKSuXOysGDB5GdnY3U1FTs2bMHEokEkZGRsLOzw9GjR3H8+HHY2tqib9++yvU+++wzJCQkYOPGjTh27BgePHiAnTt31rvdUaNG4YcffsAXX3yBrKwsfPvtt7C1tYWXlxd27NgBAMjOzkZubi5Wr14NAFi6dCk2bdqEb775BhkZGZg6dSpGjBiB33//HYAiwBowYAD69++PCxcuYPz48Zg1a5ZG+0Mul2PHjh14+PAhRCKRRusSBKEmjZzVmiCIZ4SYmBgWHR3NGGNMLpez1NRUJhaL2YwZM5TLmzZtyioqKpTrfP/99ywwMJDJ5XJlWkVFBbOysmL79+9njDHm4eHBli9frlwukUjYc889p9wWY4yFhYWxKVOmMMYYy87OZgBYampqrZ6HDx9mANjDhw+VaeXl5cza2pqdOHFCJe+4cePYW2+9xRhjbPbs2Sw4OFhl+cyZM2uU9STe3t5MJBIxGxsbJhQKGQDm5OTErl69Wuc6BEFoj9C4YRNBEDyxZ88e2NraQiKRQC6XY9iwYViwYIFyeevWrVXuNFy8eBHXrl2r0T+lvLwcOTk5KCoqQm5uLjp16qRcJhQK0b59+xqPi6q4cOECzM3NERYWprb3tWvXUFpait69e6ukV1ZWom3btgCArKwsFQ8ACA0NVav8jz76CKNHj0Zubi4++ugjTJo0CQEBAWr7EQShPhS4EAShNhEREfj6668hEong6ekJoVD1EmJjY6Py96NHj9CuXTts2bKlRlmurq5aOVhZWWm8zqNHjwAAe/fuRbNmzVSWicVirTyq4+LigoCAAAQEBOCnn35C69at0b59ewQHBze6bIIgVKE+LgRBqI2NjQ0CAgLQvHnzGkFLbbz00ku4evUq3NzclA171cfBwQEODg7w8PDA6dOnletIpVKcPXu2zjJbt24NuVyu7JvyJFV3fGQymTItODgYYrEYt27dquHh5eUFAAgKCsKZM2dUyjp16lSDdXwSLy8vDBkyBLNnz9Z4XYIgGoYCF4Ig9Mbw4cPh4uKC6OhoHD16FDdu3EBaWho++OAD3LlzBwAwZcoULFu2DLt27cKVK1cwadKket/B4uPjg5iYGIwdOxa7du1Slrlt2zYAgLe3NwQCAfbs2YN79+7h0aNHsLOzw4wZMzB16lQkJiYiJycH586dw5o1a5CYmAgAeOedd3D16lV89NFHyM7OxtatW5GQkKBVvadMmYJffvkFf/75p1brEwRRNxS4EAShN6ytrXHkyBE0b94cAwYMQFBQEMaNG4fy8nLY29sDAKZPn46RI0ciJiYGoaGhsLOzwxtvvFFvuV9//TUGDRqESZMmoWXLlpgwYQIeP34MAGjWrBkWLlyIWbNmoWnTpnj//fcBAIsWLcK8efOwdOlSBAUFoW/fvti7dy98fX0BAM2bN8eOHTuwa9cutGnTBt988w0++eQTreodHByMPn364D//+Y9W6xMEUTcCVlcPOIIgCIIgCBOD7rgQBEEQBMENFLgQBEEQBMENFLgQBEEQBMENFLgQBEEQBMENFLgQBEEQBMENFLgQBEEQBMENFLgQBEEQBMENFLgQBEEQBMENFLgQBEEQBMENFLgQBEEQBMENFLgQBEEQBMEN/wcwa9RdxLvTggAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sm2(history):\n",
    "    ivl = 0\n",
    "    ef = 2.5\n",
    "    reps = 0\n",
    "    for delta_t, rating in history:\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item() + 1\n",
    "        if rating > 2:\n",
    "            if reps == 0:\n",
    "                ivl = 1\n",
    "                reps = 1\n",
    "            elif reps == 1:\n",
    "                ivl = 6\n",
    "                reps = 2\n",
    "            else:\n",
    "                ivl = ivl * ef\n",
    "                reps += 1\n",
    "        else:\n",
    "            ivl = 1\n",
    "            reps = 0\n",
    "        ef = max(1.3, ef + (0.1 - (5 - rating) * (0.08 + (5 - rating) * 0.02)))\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "dataset['sm2_interval'] = dataset['tensor'].map(sm2)\n",
    "dataset['sm2_p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['sm2_interval'])\n",
    "cross_comparison = dataset[['sm2_p', 'p', 'y']].copy()\n",
    "\n",
    "R_Metric = mean_squared_error(cross_comparison['y'], cross_comparison['sm2_p'], squared=False) - mean_squared_error(cross_comparison['y'], cross_comparison['p'], squared=False)\n",
    "print(\"R_Metric: \", R_Metric)\n",
    "plot_brier(dataset['sm2_p'], dataset['y'], bins=40)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "cross_comparison['SM2_B-W'] = cross_comparison['sm2_p'] - cross_comparison['y']\n",
    "cross_comparison['SM2_bin'] = cross_comparison['sm2_p'].map(lambda x: round(x, 1))\n",
    "cross_comparison['FSRS_B-W'] = cross_comparison['p'] - cross_comparison['y']\n",
    "cross_comparison['FSRS_bin'] = cross_comparison['p'].map(lambda x: round(x, 1))\n",
    "\n",
    "plt.axhline(y = 0.0, color = 'black', linestyle = '-')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='SM2_bin').agg({'y': ['mean'], 'FSRS_B-W': ['mean'], 'p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of FSRS: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['p', 'mean'], sample_weight=cross_comparison_group['p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['p', 'percent'] = cross_comparison_group['p', 'count'] / cross_comparison_group['p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['FSRS_B-W', 'mean'], s=cross_comparison_group['p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['FSRS_B-W', 'mean'], label='FSRS by SM2')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='FSRS_bin').agg({'y': ['mean'], 'SM2_B-W': ['mean'], 'sm2_p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of SM2: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['sm2_p', 'mean'], sample_weight=cross_comparison_group['sm2_p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['sm2_p', 'percent'] = cross_comparison_group['sm2_p', 'count'] / cross_comparison_group['sm2_p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['SM2_B-W', 'mean'], s=cross_comparison_group['sm2_p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['SM2_B-W', 'mean'], label='SM2 by FSRS')\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.grid(linestyle='--')\n",
    "plt.title(\"SM2 vs. FSRS\")\n",
    "plt.xlabel('Predicted R')\n",
    "plt.ylabel('B-W Metric')\n",
    "plt.xlim(0, 1)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
